CN111143555A - Big data-based customer portrait generation method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of big data, and discloses a customer portrait generation method based on big data, which comprises the following steps: mapping the big customer data into a database table through hive; carrying out separation processing on information streams related to clients in a client database table by adopting separators to obtain small segments of information streams; matching key information of the client from the small information flow by a keyword matching algorithm based on preset client keywords, and establishing a label by using the key information; monitoring whether a client portrait generation request currently exists; if a client portrait generation request exists, acquiring target display information from the client portrait generation request; acquiring a target label appointed to be displayed by the target display information according to the target display information; and rendering the target label to obtain a client portrait. The client image generated by the invention meets the requirement that the user views different types of information of the client in different scenes.
Description
Technical Field
The present invention relates to the field of big data, and in particular, to a method, an apparatus, a device, and a storage medium for generating a customer portrait based on big data.
Background
The client portrait is an important application of big data technology, and the aim of the method is to establish descriptive tag attributes for users, so that real personal characteristics of the users in various aspects are sketched by using the tag attributes, and therefore, the client demands can be discovered by using the client portrait, the client preferences can be analyzed, and higher-quality and more targeted services can be provided for the clients. The traditional portrait analysis platform has the advantages of single function, high calculation cost and limited processing capacity, most of the portrait analysis platforms only display labels and portrait information, and the portrait display form is fixed and unchangeable, so that the key information of customers cannot be displayed quickly.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for generating a client portrait based on big data, and aims to solve the technical problem of generating the client portrait which can meet the requirement that a user can check different types of client information of clients in different scenes.
In order to achieve the above object, the present invention provides a method for generating a client representation based on big data, the method comprising the steps of:
mapping the big customer data into a database table through hive;
carrying out separation processing on information streams related to clients in a client database table by adopting separators to obtain small segments of information streams;
matching key information of the client from the small information flow by a keyword matching algorithm based on preset client keywords, and establishing a label by using the key information, wherein the label comprises the information of the client and the display form information of the label;
monitoring whether a client portrait generation request currently exists;
if a client portrait generation request exists, acquiring target display information from the client portrait generation request, wherein the target display information comprises information of a target label requested to be displayed;
acquiring a target label appointed to be displayed by the target display information according to the target display information;
and rendering the target label to obtain a client portrait.
Preferably, after the step of matching key information of the client from the short segment of information stream by a keyword matching algorithm based on the preset client keyword and establishing a label with the key information, the method further comprises:
according to a preset label classification rule, carrying out class marking on the label to obtain a label with a class mark;
and storing the labels according to the category labels in a classified manner to obtain a first label database.
Preferably, the obtaining, according to the target display information, the target tag specified to be displayed by the target display information includes:
analyzing the target display information to obtain information of a target label appointed to be displayed in the target display information;
according to the information, inquiring and accessing a second label database of the category of the target label;
extracting the target tag from the second tag database.
Preferably, after the step of rendering the target tag to obtain the customer representation, the method further includes:
counting the viewed times of the target label through a buried point to obtain the historical viewed times of the target label;
and displaying the label with the most historical viewing times according to the historical viewing times and the display number of the preset labels.
Preferably, the rendering the target display tag to obtain the customer representation includes:
rendering the data of the target tag according to a preset JSP template to obtain a client portrait frame;
and calculating the position coordinates of the target label on the client portrait frame according to the display form information of the target label and arranging the target label according to the position coordinates to obtain the client portrait.
Further, to achieve the above object, the present invention provides a client representation generating apparatus based on big data, comprising:
the mapping module is used for mapping the client big data into a database table through hive;
the separation module is used for separating information streams related to clients in the client database table by using separators to obtain small segments of information streams;
the matching module is used for matching key information of the client from the small information flow through a keyword matching algorithm based on preset client keywords and establishing a label according to the key information, wherein the label comprises the information of the client and the display form information of the label;
the monitoring module is used for monitoring whether a client portrait generation request exists at present;
the system comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is used for acquiring target display information from a client portrait generation request if the client portrait generation request exists, and the target display information comprises information of a target label requested to be displayed;
the second acquisition module is used for acquiring a target label appointed to be displayed by the target display information according to the target display information;
and the rendering module is used for rendering the target label to obtain the client portrait.
Preferably, the client representation generating apparatus further comprises:
the marking module is used for carrying out category marking on the labels according to a preset label classification rule to obtain labels with category marks;
and the storage module is used for storing the labels according to the category labels in a classified manner to obtain a first label database.
Preferably, the second obtaining module includes:
the analysis unit is used for analyzing the target display information to obtain the information of the target label appointed to be displayed in the target display information;
the query unit is used for querying and accessing a second label database of the category of the target label according to the information;
an extracting unit, configured to extract the target tag from the second tag database.
Preferably, the client representation generating apparatus further comprises:
the counting module is used for counting the viewed times of the target label through a buried point to obtain the historical viewed times of the target label;
and the display module is used for displaying the label with the most historical viewing times according to the historical viewing times and the display number of the preset labels.
Preferably, the rendering module comprises:
the rendering unit is used for rendering the data of the target tag according to a preset JSP template to obtain a client portrait frame;
and the calculating unit is used for calculating the position coordinates of the target label on the client portrait frame according to the display form information of the target label and arranging the position coordinates to obtain the client portrait.
Further, to achieve the above object, the present invention also provides a big data based client representation generating device comprising a memory, a processor and a client representation generating program stored on the memory and executable on the processor, the client representation generating program when executed by the processor implementing the steps of the client representation generating method as described in any one of the above.
Further, to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a client representation generation program, which when executed by a processor, implements the steps of the client representation generation method as recited in any one of the above.
The method comprises the steps of mapping big data of a client into a database table through hive, dividing the information flow of the client into small information flows by using separators, matching key information of the client from the small information flows by using a keyword matching algorithm, establishing a label of the client according to the key information, monitoring whether a client portrait generation request exists at present, obtaining a corresponding target label according to the request if the client portrait generation request exists, rendering the target label, and generating the client portrait.
Drawings
FIG. 1 is a schematic diagram of an operating environment of a big data based client representation generation device according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating a first embodiment of a big data-based client representation generation method according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of a big data based client representation generation method according to the present invention;
FIG. 4 is a schematic diagram illustrating a detailed flow of step S60 in FIG. 2;
FIG. 5 is a schematic flow chart illustrating a third embodiment of a big data-based client representation generation method according to the present invention;
FIG. 6 is a schematic diagram illustrating a detailed flow of step S70 in FIG. 2;
FIG. 7 is a block diagram of an embodiment of a big data based client representation generation apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a big data-based customer representation generation device.
Referring to FIG. 1, FIG. 1 is a schematic diagram of an operating environment of a client representation generation device according to an embodiment of the present invention.
As shown in FIG. 1, the client representation generating apparatus comprises: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the hardware configuration of the client representation generating device illustrated in FIG. 1 is not intended to be limiting, and may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer-readable storage medium, may include therein an operating system, a network communication module, a user interface module, and a computer program. The operating system is a program that manages and controls the client representation generation facility and software resources, supporting the execution of the client representation generation program, as well as other software and/or programs.
In the hardware configuration of the client representation generating apparatus shown in FIG. 1, the network interface 1004 is mainly used for accessing a network; the user interface 1003 is mainly used for detecting a confirmation instruction, an editing instruction, and the like. And processor 1001 may be configured to invoke the client representation generation program stored in memory 1005 and perform the operations of the various embodiments of the client representation generation method described below.
Various embodiments of the client representation generation method of the present invention are presented based on the hardware architecture of the above-described big data based client representation generation device.
Referring to FIG. 2, FIG. 2 is a flow chart illustrating a first embodiment of a big data-based client representation generation method according to the present invention. In this embodiment, the method for generating a client representation includes the following steps:
step S10: mapping the big customer data into a database table through hive;
in this embodiment, a database table is established, and the big client data is mapped by hive to obtain the database table of the big client data, so as to provide query conditions for obtaining the next client information. And based on a database table of the big data of the client, performing data query by using SQL-like hiveQL language, wherein all hive data are stored in a Hadoop-compatible file system. When the client big data is loaded, the client big data is moved to the directory with hive set in hdfs. The big client data are stored in the relational database, so that the time for performing semantic check during query is greatly reduced, and the data stored in the Hadoop file system can be directly used.
Step S20: carrying out separation processing on information streams related to clients in a client database table by adopting separators to obtain small segments of information streams;
in this embodiment, Hive has no special data storage format and also has no index established for data, and a user can customize a table in Hive, set column separators and row separators in Hive when creating the table, control the separators to separate client information streams in a client database table, and separate the client information streams into small segments of information streams.
Furthermore, in the configuration file corresponding to the row separator or the column separator of the hive, the statement separator separates at the specific character, when the separation is needed, the character recognition is firstly carried out on the separation information to be treated, and if the preset separation word is recognized, the separation is carried out through the separator.
For example, the "age 28 of the professional programmer for a certain sex in the name liu" is partitioned into small information streams of the "age 28" of the professional programmer for a certain sex in the name liu ", the" male sex in the name liu ", the" professional programmer for a certain sex in the name liu ", and the" age 28 "according to the preset partition keywords. In addition, the client big data also comprises the position information of the client, such as the city, the residential address and the like; work information such as company address, job position, etc.; asset information such as famous property, car property and all other assets of various individuals; insurance information such as information of the insurance type, the time of the insurance, the amount of the insurance, the rate of settlement of the claim, etc.
Step S30: matching key information of the client from the small information flow by a keyword matching algorithm based on preset client keywords, and establishing a label by using the key information, wherein the label comprises the information of the client and the display form information of the label;
in this embodiment, a keyword library is preset, keyword words are preset, matching is performed from a small segment of information stream through a keyword matching algorithm, whether a matching relationship exists between the separated small segment of information stream and the preset keyword words in the word library is judged, if the preset keyword words exist in the small segment of information stream in a matching manner, the small segment of information stream is extracted, the small segment of information stream is determined to be the key information of a client, and a label of the client is established according to the key information of the small segment of information stream. The key words are set in a user-defined mode according to user requirements; when the information label is established, a user can set a display form of the label according to the key information type of the information label or the preference of the user, and the display form of the label can be that the label is regularly transversely or longitudinally arranged, irregularly tiled and the like. In addition, the keyword matching algorithm includes kmp algorithm, which requires a keyword library to be pre-established before setting related keyword in the keyword library for providing comparison initial data for matching keyword information.
Further, after key information is determined, an information label of a customer is formulated according to the key information. The key information comprises basic attributes, asset characteristics, interests, shopping interests and demand characteristics of the customers, and the key information is used as an information tag of the customers to identify the characteristics of the customers. One label is typically an artificially specified highly refined signature, such as an age group label: age 25, territorial label: the label exhibits two important features, guangdong: semantization, people can conveniently and quickly understand the meaning of each label, so that the customer image has practical significance, the service requirement of a user is well met, and the user can provide and develop corresponding services according to the customer attribute; in short text, each label usually only represents one meaning, in other words, the detailed information of the client is extracted, the key information is extracted, and the key words capable of summarizing the whole piece of key information are extracted from the key information to represent the attributes of the client.
For example, if a matching keyword preset in the keyword matching algorithm is "name", when a small information stream of a certain client is called to be matched, if information such as "name XXX" is matched in words of the small information stream, the small information stream is extracted to serve as key information of the client, and a tag of the client is established.
Step S40: monitoring whether a client portrait generation request currently exists;
step S50: if a client portrait generation request exists, acquiring target display information from the client portrait generation request, wherein the target display information comprises information of a target label requested to be displayed;
in the embodiment, whether a client portrait generation request exists on a current display interface is monitored, if the client portrait generation request exists on the current display interface, request information carried in the display request is analyzed, target display information is determined from the request information, information of a target label requested to be displayed is obtained from the target display information, a label database of the type of the target display label is inquired, an access path of the label database is obtained and accessed, and then the target label is extracted.
According to different application scenes, a plurality of information label categories can be associated with the same working link, if an information label indicating that the current working link is relevant to be displayed exists in the display request, the information label category information preset and associated with the working link needs to be acquired first, then an access path of a category label database appointed in the information label category information is acquired from a central server, and the information label database is accessed according to the access path to acquire the relevant information label for display.
Step S60: acquiring a target label appointed to be displayed by the target display information according to the target display information;
in this embodiment, tag information to be displayed is acquired from the target display information obtained through analysis, and according to the tag information, a tag database of a category in which a target tag pointed in the tag information is located is accessed, so that the target tag is acquired from the tag database.
Step S70: and rendering the target label to obtain a client portrait.
In this embodiment, it is preferable to render the data of the target tag in the form of a hash table according to a corresponding JSP template, calculate position coordinates of the data in a rendered customer portrait frame, and arrange the position coordinates in a corresponding manner, thereby obtaining a final customer portrait. The display form of the label can be set by a user in a self-defined way besides the initially set display form. For example, the user can set the display position, sequence, shape of the information tag; furthermore, a user can hide part of the tags, and the tags can be checked by clicking display when the tags need to be checked, so that visual interference is avoided.
For example, basic information tag data reflecting client basic information is obtained, the data is returned to a server in a hash table mode, the server renders the data according to a corresponding JSP template, corresponding client portrait frame graphs are generated through rendering and returned to a display interface, position coordinates of preset display modes of the information tags in the data in the client portrait frame graphs are calculated and arranged according to the position coordinates, and a final client portrait is obtained. The frame graph of the customer portrait can be set by self-definition, and can be a portrait or a geometric graph, which is determined according to actual conditions.
Furthermore, the information labels of the same customer have the same identification, the information labels of the same customer have an incidence relation in information label databases of different types, when the information label of the customer needs to be displayed, all incidence information of the information label type of the customer is obtained, then the information label is displayed according to a preset display form, if the information is too much, a user can selectively control and display a part of information labels according to the requirement, and hide a part of information labels, so that the user can quickly know the information of the customer, the relevant service content can be conveniently formulated for the customer, and the service quality is improved.
According to the method, the big data of the client are mapped into the database table through hive, the information flow of the client is divided into small information flows by adopting separators, then key information of the client is matched from the small information flows by a keyword matching algorithm, a label of the client is established according to the key information, whether a client portrait generation request exists at present is monitored, if the client portrait generation request exists, a corresponding target label is obtained according to the request, then the target label is rendered, and the client portrait is generated.
Referring to FIG. 3, FIG. 3 is a flow chart illustrating a second embodiment of a big data-based client representation generation method according to the present invention. In this embodiment, after step S30, the method further includes:
step S001: according to a preset label classification rule, carrying out class marking on the label to obtain a label with a class mark;
in this embodiment, according to different application scenarios, a tag classification rule is set, tags are divided into different categories, and category labels are established for the tags one by one according to the categories, so as to obtain tags with category labels. For example, a category is established according to a sales scene, and in an insurance sales scene, according to insurance information of a client, the following sales service links are divided during service: insurance renewal link, claim settlement processing link, insurance sales link and the like. For example, in the insurance renewal link, basic information, insured information and renewal information of a client need to be checked, and the label of the client is subjected to category marking according to the categories of the information represented by the label, such as the categories of the basic information, the insurance information and the renewal information, so as to obtain the label with the category marking.
Step S002: and storing the labels according to the category labels in a classified manner to obtain a first label database.
In this embodiment, after the tags of all the categories are classified according to the categories, further, the tags of the same customer are associated, so that when the tags of one category of one customer need to be displayed, all the tags of the one category of the customer are obtained.
In this embodiment, the tags are stored in a classified manner according to the category labels, and the tags of the same category label are stored in the same database, so as to obtain tag databases of different categories. The database for storing the tags selects the HBASE database. In addition, the specific category of the tag is determined by the actual application scene and can be set by the user in a customized manner according to different application scenes.
For example, in an application scenario of insurance promotion, if an agent is in a client renewal link and needs basic information, insurance information and asset information of a client, a category label is established for a label corresponding to the basic information, a label corresponding to the insurance information and a label corresponding to the asset information of the client, and then the labels are stored in a database to obtain a label database of the basic information label, the bid information label and the asset information label.
Referring to fig. 4, fig. 4 is a schematic view of a detailed flow of the step S60 in fig. 2. Based on the above embodiment, in the present embodiment, step S60 further includes:
step S601: analyzing the target display information to obtain information of a target label appointed to be displayed in the target display information;
in this embodiment, the request information of the display request is analyzed, the target display information in the request is obtained, and the type of the information tag which is specified to be displayed in the target display information is determined, so that the information tag database of the corresponding type is accessed, and the information tag corresponding to the client is extracted.
Step S602: according to the information, inquiring and accessing a second label database of the category of the target label;
in this embodiment, according to the target display information in the above step, the tag database of the category where the tag to be displayed is located is queried, the access path corresponding to the tag database of the category is obtained, and the tag database of the category is accessed according to the access path.
Step S603: and acquiring the target label from the second label database.
In this embodiment, based on the access path of the second tag database obtained in the above step, the corresponding tag database is accessed, and the target tag with the display requirement is extracted from the tag database. For example, after determining that the tag to be displayed is a tag of the basic information category, extracting the tag of the basic information category of the client which is currently queried from the tag database of the basic information category.
Referring to FIG. 5, FIG. 5 is a flow chart illustrating a third embodiment of a big data-based client representation generation method according to the present invention. Based on the above embodiment, in this embodiment, after step S70, the method further includes:
step S010: counting the viewed times of the target label through a buried point to obtain the historical viewed times of the target label;
in this embodiment, the number of times each tag is viewed is recorded in real time by performing embedding processing on the tags, so that the historical viewing number of times each tag is viewed is obtained. The method is not limited to the point burying mode, for example, the modes of manual point burying, visual point burying, automatic point burying and the like are adopted. Meanwhile, the implantation position of the embedded point code is not limited, for example, embedding points in a front-end UI layer and embedding points in a bottom-layer data table or log.
Furthermore, embedding a buried point code in the display position of the information tag in advance, when a user clicks and checks, the buried point code automatically calls a relevant interface to upload buried point data to a rear-end server, and the rear-end server obtains the historical checking times of the tag by counting the buried point data.
Step S020: and displaying the label with the most historical viewing times according to the historical viewing times and the display number of the preset information labels.
In the embodiment, the labels of the same category of the same client are sorted based on the statistical data of the historical viewing times, the label with the largest historical viewing times indicates that the user frequently views the labels, and then the label with the largest historical viewing times is arranged in the priority display queue according to the historical viewing times to display the label with the largest historical viewing times, wherein the labels with the larger historical viewing times prove that the more frequency of viewing is increased, which indicates that the user frequently needs the labels, so that the priority display of the labels is set, and the user can acquire the required client information at the highest speed.
For example, the number of the displayed labels of a preset category is 5, the historical viewing times of the labels of the category are counted, the labels are arranged from high to low according to the historical viewing times, the 5 labels with the viewing times ranked 5 top are displayed, and the rest labels are hidden. Further, the user can view all the hidden tags in the rest part by clicking and displaying, and if the user viewing request is not monitored, 5 tags with high historical viewing times are displayed by default.
Referring to fig. 6, fig. 6 is a schematic view of a detailed flow of the step S70 in fig. 2. Based on the above steps, in this embodiment, step S70 further includes:
step S701: rendering the data of the target tag according to a preset JSP template to obtain a client portrait frame;
step S702: and calculating the position coordinates of the target label on the client portrait frame according to the label display form information of the target label and arranging the target label according to the position coordinates to obtain the client portrait.
In this embodiment, when the information tag is established, the user sets the display form of the tag according to the key information type of the tag or the preference of the user, and the display form of the tag may be that the tag is regularly arranged in the horizontal direction or the longitudinal direction, irregularly tiled, and the like. Preferably, the data of the tag to be displayed is returned to the server in a hash table form, and the server renders according to a JSP template corresponding to the data, wherein the JSP template is a preset Java server page template to obtain a client portrait frame, and calculates the position coordinates of the tag in the client portrait frame according to display form information configured in advance in the tag and arranges the tag correspondingly according to the position coordinates, so as to obtain the final client portrait.
For example, basic information tag data reflecting client basic information is obtained, the data is returned to a server in a hash table mode, the server renders the data according to a preset JSP template, corresponding client portrait frame graphs are generated through rendering and returned to a display interface, then position coordinates of the preset display forms of the information tags in the data in the client portrait frame graphs are calculated according to display form information configured in advance in the tags, and the position coordinates are arranged to obtain the final client portrait. The frame graph of the customer portrait can be set by self-definition, and can be a portrait or a geometric graph, which is determined according to actual conditions.
The invention also provides a client portrait generation device based on the big data.
Referring to FIG. 7, FIG. 7 is a block diagram of a client representation generation apparatus according to an embodiment of the present invention. In this embodiment, the client representation generation apparatus includes:
the mapping module 10 is used for mapping the customer big data into a database table through hive;
the separation module 20 is configured to perform separation processing on information streams related to clients in a client database table by using separators to obtain small segments of information streams;
the matching module 30 is configured to match key information of the client from the short segment of information stream through a keyword matching algorithm based on a preset client keyword, and establish a tag according to the key information, where the tag includes information of the client and information of a display form of the tag;
a monitoring module 40 for monitoring whether a client portrait generation request currently exists;
a first obtaining module 50, configured to, if there is a client representation generation request, obtain target display information from the client representation generation request, where the target display information includes information of a target tag requested to be displayed;
a second obtaining module 60, configured to obtain, according to the target display information, a target tag that is specified to be displayed by the target display information;
and the rendering module 70 is configured to perform rendering processing on the target tag to obtain a client portrait.
In this embodiment, the mapping module 10 maps the big customer data into a database table by hive, the separating module 20 separates information streams related to customers in the customer database table by separators to obtain small segments of information streams, the matching module 30, is used for matching the key information of the client from the short information flow by a key word matching algorithm based on the preset client key words, and builds a tag with the key information, the monitoring module 40 monitors whether a client representation generation request currently exists, the first obtaining module 50 determines that a client representation generation request exists, then, the target display information is obtained from the client portrait generation request, the second obtaining module 60 obtains the target label specified to be displayed by the target display information according to the target display information, and the rendering module 70 performs rendering processing on the target label to obtain the client portrait.
The invention also provides a computer readable storage medium.
In this embodiment, the computer readable storage medium has stored thereon a client representation generation program which, when executed by a processor, implements the steps of the client representation generation method as described in any one of the above embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM), and includes instructions for causing a terminal (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The present invention is described in connection with the accompanying drawings, but the present invention is not limited to the above embodiments, which are only illustrative and not restrictive, and those skilled in the art can make various changes without departing from the spirit and scope of the invention as defined by the appended claims, and all changes that come within the meaning and range of equivalency of the specification and drawings that are obvious from the description and the attached claims are intended to be embraced therein.
Claims (10)
1. A method for big data based generation of a client representation, said method comprising the steps of:
mapping the big customer data into a database table through hive;
carrying out separation processing on information streams related to clients in a client database table by adopting separators to obtain small segments of information streams;
matching key information of the client from the small information flow by a keyword matching algorithm based on preset client keywords, and establishing a label by using the key information, wherein the label comprises the information of the client and the display form information of the label;
monitoring whether a client portrait generation request currently exists;
if a client portrait generation request exists, acquiring target display information from the client portrait generation request, wherein the target display information comprises information of a target label requested to be displayed;
acquiring a target label appointed to be displayed by the target display information according to the target display information;
and rendering the target label to obtain a client portrait.
2. A client representation generation method as claimed in claim 1, wherein after said step of matching client key information from said short segment information stream by a keyword matching algorithm based on preset client keywords and building labels from said key information, further comprising:
according to a preset label classification rule, carrying out class marking on the label to obtain a label with a class mark;
and storing the labels according to the category labels in a classified manner to obtain a first label database.
3. The method of generating a client representation as recited in claim 2, wherein the obtaining an object tag specified to be displayed by the object presentation information based on the object presentation information comprises:
analyzing the target display information to obtain information of a target label appointed to be displayed in the target display information;
according to the information, inquiring and accessing a second label database of the category of the target label;
extracting the target tag from the second tag database.
4. A method for generating a client representation as recited in claim 1, wherein subsequent to the step of rendering the object tag to obtain a client representation, further comprising:
counting the viewed times of the target label through a buried point to obtain the historical viewed times of the target label;
and displaying the label with the most historical viewing times according to the historical viewing times and the display number of the preset labels.
5. The method of generating a client representation as recited in claim 1, wherein rendering the target presentation tag to obtain a client representation comprises:
rendering the data of the target tag according to a preset JSP template to obtain a client portrait frame;
and calculating the position coordinates of the target label on the client portrait frame according to the display form information of the target label and arranging the target label according to the position coordinates to obtain the client portrait.
6. A big data based client representation generation apparatus, said client representation generation apparatus comprising:
the mapping module is used for mapping the client big data into a database table through hive;
the separation module is used for separating information streams related to clients in the client database table by using separators to obtain small segments of information streams;
the matching module is used for matching key information of the client from the small information flow through a keyword matching algorithm based on preset client keywords and establishing a label according to the key information, wherein the label comprises the information of the client and the display form information of the label;
the monitoring module is used for monitoring whether a client portrait generation request exists at present;
the system comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is used for acquiring target display information from a client portrait generation request if the client portrait generation request exists, and the target display information comprises information of a target label requested to be displayed;
the second acquisition module is used for acquiring a target label appointed to be displayed by the target display information according to the target display information;
and the rendering module is used for rendering the target label to obtain the client portrait.
7. The client representation generating apparatus of claim 6, wherein said second obtaining module comprises:
the analysis unit is used for analyzing the target display information to obtain the information of the target label appointed to be displayed in the target display information;
the query unit is used for querying and accessing a second label database of the category of the target label according to the information;
and the extracting unit is used for extracting the target label from the second label database.
8. The client representation generation apparatus of claim 6, wherein the rendering module comprises:
the rendering unit is used for rendering the data of the target tag according to a preset JSP template to obtain a client portrait frame;
and the calculating unit is used for calculating the position coordinates of the target label on the client portrait frame according to the display form information of the target label and arranging the position coordinates to obtain the client portrait.
9. A big data based client representation generating device, comprising a memory, a processor, and a client representation generating program stored on said memory and executable on said processor, said client representation generating program when executed by said processor implementing the steps of the big data based client representation generating method as claimed in any one of claims 1 to 5.
10. A computer-readable storage medium, having stored thereon, a client representation generation program, which when executed by a processor, performs the steps of the big data based client representation generation method as claimed in any of claims 1-5.
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