CN112270605A - Information processing method and device for insurance business - Google Patents
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Abstract
The disclosure discloses an information processing method and device for insurance business, which comprises the steps of obtaining flow user behavior data; analyzing the flow user behavior data in a preset dimension to obtain analyzed data; matching the analyzed data with preset data in a preset data set, and determining matched data matched with the analyzed data in the preset data set; determining the label mapped with the matching data as a label of the traffic user behavior data; and combining the labels of the flow user behavior data according to a preset mode to obtain a user portrait. By constructing a user profile based on traffic user behavior data, accurate services can be implemented in insurance services.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an information processing method and apparatus for insurance services.
Background
The traffic user has social behaviors among user individuals, social behaviors cause an aggregation effect, and the user with the aggregation effect in a certain scale can be called the traffic user.
In insurance product related business, for example, insurance sales or insurance customization, insurance agents or insurance customizers cannot precisely match insurance services for different users.
Disclosure of Invention
The present disclosure is mainly directed to provide an information processing method and apparatus for insurance services, so as to solve the problem that insurance services cannot be accurately matched for different users.
In order to achieve the above object, according to a first aspect of the present disclosure, there is provided an information processing method for insurance business, including: acquiring flow user behavior data; analyzing the flow user behavior data in a preset dimension to obtain analyzed data; matching the analyzed data with preset data in a preset data set to determine matched data matched with the analyzed data in the preset data set; determining the label mapped with the matching data as a label of the traffic user behavior data; and combining the labels of the traffic user behavior data according to a preset mode to obtain a user portrait.
Optionally, analyzing the traffic user behavior data with a preset dimension, and obtaining analyzed data includes: performing data extraction on the traffic user behavior data according to a preset dimension to obtain extracted data; based on the extracted data, generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy; and determining a rule data set formed by the rule data as analyzed data.
Optionally, matching the analyzed data with preset data in a preset data set, and determining matching data in the preset data set, which matches the analyzed data, includes: matching the analyzed data with preset data in a preset data set, and determining preset data which are the same as the analyzed data; and determining the preset data which is the same as the analyzed data as matching data.
Optionally, determining the label mapped with the matching data as the label of the traffic user behavior data includes: acquiring a pre-established label set, wherein labels in the label set are associated with at least one piece of preset data; and determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
Optionally, the obtaining of the traffic user behavior data includes: acquiring flow user behavior data from a preset position; storing the traffic user behavior data in a database; and acquiring the flow user behavior data from a database.
According to a second aspect of the present disclosure, there is provided an information processing apparatus for insurance business, including: the acquiring unit acquires flow user behavior data; the analysis unit is used for analyzing the preset dimensionality of the flow user behavior data to obtain analyzed data; the matching unit is used for matching the analyzed data with preset data in a preset data set so as to determine matching data matched with the analyzed data in the preset data set; the determining unit is used for determining the label mapped with the matching data as a label of the traffic user behavior data; and the combination unit is used for combining the labels of the traffic user behavior data according to a preset mode to obtain a user portrait.
Optionally, the parsing unit includes: the extraction module is used for extracting the data of the flow user behavior data according to a preset dimension to obtain extracted data; the generating module is used for generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy based on the extracted data; and the determining module is used for determining a rule data set formed by the rule data as the analyzed data.
Optionally, the matching unit comprises: the matching module is used for matching the analyzed data with preset data in a preset data set and determining the preset data which is the same as the analyzed data; and the first determining module is used for determining the preset data which is the same as the analyzed data as matching data.
Optionally, the determining unit includes: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module acquires a pre-established label set, and labels in the label set are associated with at least one piece of preset data; and the second determination module is used for determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
Optionally, the obtaining unit includes: the first acquisition module is used for acquiring flow user behavior data from a preset position; the storage module is used for storing the flow user behavior data into a database; and the second acquisition module is used for acquiring the flow user behavior data from a database.
In the embodiment of the disclosure, the user behavior data is obtained through the flow; analyzing the flow user behavior data in a preset dimension to obtain analyzed data; matching the analyzed data with preset data in a preset data set, and determining matched data matched with the analyzed data in the preset data set; determining the label mapped with the matching data as a label of the traffic user behavior data; and combining the labels of the flow user behavior data according to a preset mode to obtain a user portrait. By constructing the user portrait based on the flow user behavior data, accurate service can be realized in insurance business, and the problem that the prior art cannot accurately match insurance service for different users is solved.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of an information processing method for insurance services according to an embodiment of the present disclosure;
fig. 2 is an application scenario diagram of an information processing method for insurance services according to an embodiment of the present disclosure;
FIG. 3 is a schematic structural diagram of an information processing apparatus for insurance services according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those skilled in the art, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure may be described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
An exemplary system architecture to which embodiments of the information processing method or apparatus for insurance services of the present application may be applied may include a terminal device, a network, and a server. The network serves as a medium for providing a communication link between the terminal device and the server. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use a terminal device to interact with a server over a network to receive or send messages, etc. Various client applications, such as an insurance platform APP for completing insurance services, may be installed on the terminal device.
The terminal device may be various electronic devices having a display screen and supporting browsing of Picture and text information, including but not limited to a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, mpeg Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, mpeg Audio Layer 4), a laptop portable computer, a desktop computer, and the like.
The server may be a server that provides various services, such as providing analysis services for behavior data generated by a user through a terminal device.
It should be noted that the information processing method for insurance business provided by the embodiment of the present application is generally executed by a server, and accordingly, the information processing apparatus for insurance business is generally disposed in the server.
According to an embodiment of the present disclosure, there is provided an information processing method for insurance business, as shown in fig. 1, the method includes the following steps 101 to 105:
step 101: and acquiring flow user behavior data.
In this embodiment, the execution subject may obtain traffic user behavior data from the database, where the traffic user behavior data includes user information and/or user insurance data.
Specifically, the user information data includes user parking mode data and personal information (third-party application authorization information, various scenized invitation code link information, user real-name authentication information, and the like); data such as liveness of a user in an insurance marketing system, task completion condition, user popularization and customer extension condition, user plan book sharing, opinion and suggestion of the user in the using process and the like; the memorandum of the user records the client requirement and intention information; important item information of the user's notes in the calendar.
The user insurance data can comprise vehicle insurance data such as vehicle insurance application insurance types, insurance policy insurance cycle, vehicle insurance claim settlement information, vehicle insurance refund data and the like; browsing, sharing, purchasing, refunding and other life insurance data of products such as life insurance, accident insurance, non-vehicle insurance and the like; the user browses, learns, shares and collects insurance behavior data related to insurance related industries; the user pays attention to, browses, purchases, evaluates insurance data such as insurance products.
As an optional implementation manner of this embodiment, the acquiring traffic user behavior data includes: acquiring flow user behavior data from a preset position; storing flow user behavior data into a database; and acquiring flow user behavior data from a database.
In this embodiment, the executing agent may obtain information constituting the traffic user behavior data from a location (e.g., including but not limited to software, storage, memo, calendar notes, etc.) of the user side that is allowed to access; the information forming the flow user behavior data can be obtained from the log or the cache of the server; then storing the acquired traffic user behavior data into a database; after receiving a request for analyzing the traffic user behavior (which may be a request for customizing insurance products for a user) sent by a user terminal, the traffic user behavior data may be obtained from a database.
In the embodiment, the data base is provided for accurately finishing the user portrait by collecting very comprehensive data from the preset position, and the data source comprises user information and/or user insurance data so that the portrait for the user can be realized in insurance business.
Step 102: and analyzing the preset dimensionality of the flow user behavior data to obtain analyzed data.
In this embodiment, before analyzing the traffic user behavior data, the method further includes integrating the traffic user behavior data with a user (or a user end device, a behavior mode, and the like) as a unit to obtain the traffic user behavior data stored in a structured manner. The execution main body can analyze the flow user behavior data stored in the structured mode according to the preset dimensionality by using the preset analysis rule to obtain the analyzed data. The preset dimensions may include, but are not limited to, a time dimension, an insurance type information dimension, an insurance-related information dimension, a user characteristic information dimension, and the like. For example, the analysis is performed according to a time dimension, which may be analysis of flow user behavior data within a preset time; analyzing according to insurance type information dimension, which can be according to insurance type dimension such as vehicle insurance type, life insurance type and the like; the analysis may be performed according to the dimension of information related to the insurance content, or may be performed according to the dimension information of attribute information required to constitute the insurance.
Specifically, according to the preset dimensionality, the flow user behavior data is analyzed by using the preset analysis rule from multiple dimensionalities through analysis operations such as slicing, aggregating, drilling, rotating and the like on the flow user behavior data organized in a multidimensional form, so that the data can be analyzed, and the information and the connotation contained in the data can be embodied from multiple dimensionalities, multiple sides and multiple data integration degrees.
As an optional implementation manner of this embodiment, analyzing the traffic user behavior data with a preset dimension, and obtaining the analyzed data includes: performing data extraction on the traffic user behavior data according to a preset dimension to obtain extracted data; based on the extracted data, generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy; and determining a rule data set formed by the rule data as analyzed data.
In this embodiment, the execution main body may perform data extraction on the traffic user behavior data according to a preset dimension to obtain extracted data, where the extraction rule is implemented based on a preset algorithm, and after the extracted data is obtained through extraction, granular traffic user behavior rule data may be generated for the extracted data by using a preset policy (for example, the extracted data may be implemented to correspond to the granular traffic user behavior rule data based on an association algorithm). The rule data of the granular traffic user behavior data is a feature for representing the content of the traffic user behavior data; and determining the rule data of the granular traffic user behavior data as analysis data.
Step 103: and matching the analyzed data with preset data in a preset data set, and determining matched data matched with the analyzed data in the preset data set.
In this embodiment, the rule data of the granular traffic user behavior data may be matched with preset rule data in a preset rule data set, and if the rule data of the granular traffic user behavior data matches with the preset rule data, matching data that matches the analyzed data in the preset data set. The preset rule data set comprises a plurality of preset rule data, and the plurality of preset rule data are a plurality of preset characteristic field data.
As an optional implementation manner in this embodiment, matching the analyzed data with preset data in a preset data set, and determining matching data in the preset data set that matches the analyzed data includes: matching the analyzed data with preset data in a preset data set, and determining preset data which are the same as the analyzed data; and determining preset data which is the same as the analyzed data as matching data.
In this embodiment, if the feature used for characterizing the content of the traffic user behavior data is the same as the plurality of preset feature field data, the feature field data, which is the same as the feature used for characterizing the content of the traffic user behavior data, of the plurality of feature field data is determined as the matching data.
Step 104: and determining the label mapped with the matching data as the label of the traffic user behavior data.
In this embodiment, a mapping relationship between the preset data in the preset data set and the preset tag may be pre-established, and the execution subject determines the tag mapped with the matching data based on the matching data determined in step 103.
As an optional implementation manner of this embodiment, determining, as the tag of the traffic user behavior data, the tag mapped with the matching data includes: acquiring a pre-established label set, wherein labels in the label set are associated with at least one piece of preset data; and determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
Referring to fig. 2, fig. 2 shows a correspondence relationship between a tag and preset rule data, where the tag includes "license plate, business risk start, score, and strong risk start. A set of simple pre-set rule data "sua brand and score > 90and Changsha Chinese vehicle model (ABBLK) and non-Yue U brand".
Step 105: and combining the labels of the flow user behavior data according to a preset mode to obtain a user portrait.
One user is presented with a plurality of labels, one label contains a plurality of rules, one user has a plurality of labels, one user corresponds to a plurality of rule sets,
in this embodiment, after determining the tags of the multiple traffic user behavior data, the tags of the traffic user behavior data may be sent to the user side, so that the user determines a combination manner of the tags through the user side, or combines the tags according to a preset combination manner, so as to obtain the user portrait finally. It can be seen that each user portrait corresponds to a plurality of tags, because each tag corresponds to a plurality of rule data, and the plurality of rule data constitute a rule data set, each user portrait corresponds to a plurality of rule data sets. Based on the user representation obtained, a precise service can be realized in insurance business, for example, based on the user representation, insurance customization can be realized or based on the user representation, sales behavior can be precisely realized.
From the above description, it can be seen that the present disclosure achieves the following technical effects: obtaining flow user behavior data; analyzing the flow user behavior data in a preset dimension to obtain analyzed data; matching the analyzed data with preset data in a preset data set, and determining matched data matched with the analyzed data in the preset data set; determining the label mapped with the matching data as a label of the traffic user behavior data; and combining the labels of the flow user behavior data according to a preset mode to obtain a user portrait. By constructing a user profile based on traffic user behavior data, accurate services can be implemented in insurance services.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
According to an embodiment of the present disclosure, there is also provided an apparatus for implementing the information processing method for insurance services, as shown in fig. 3, the apparatus includes: an obtaining unit 301, which obtains traffic user behavior data; the analysis unit 302 is configured to perform preset dimension analysis on the traffic user behavior data to obtain analyzed data; the matching unit 303 matches the analyzed data with preset data in a preset data set to determine matching data in the preset data set, which matches the analyzed data; a determining unit 304, configured to determine a tag mapped with the matching data as a tag of traffic user behavior data; a combining unit 305, for combining the labels of the traffic user behavior data according to a preset mode to obtain a user portrait.
Optionally, the parsing unit 302 includes: the extraction module is used for extracting the data of the flow user behavior data according to a preset dimension to obtain extracted data; the generating module is used for generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy based on the extracted data; and the determining module is used for determining a rule data set formed by the rule data as the analyzed data.
Optionally, the matching unit 303 includes: the matching module is used for matching the analyzed data with preset data in a preset data set and determining the preset data which is the same as the analyzed data; and the first determining module is used for determining the preset data which is the same as the analyzed data as matching data.
Optionally, the determining unit 304 includes: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module acquires a pre-established label set, and labels in the label set are associated with at least one piece of preset data; and the second determination module is used for determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
Optionally, the obtaining unit 301 includes a first obtaining module, and obtains the traffic user behavior data from a preset location; the storage module is used for storing the flow user behavior data into a database; and the second acquisition module is used for acquiring the flow user behavior data from a database.
The disclosed embodiment provides an electronic device, as shown in fig. 4, which includes one or more processors 41 and a memory 42, and one processor 43 is taken as an example in fig. 4.
The controller may further include: an input device 43 and an output device 44.
The processor 41, the memory 42, the input device 43 and the output device 44 may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.
The processor 41 may be a Central Processing Unit (CPU). The processor 41 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 42, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the control methods in the embodiments of the present disclosure. The processor 41 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 42, that is, implements the information processing method for insurance services of the above-described method embodiments.
The memory 42 may 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; the storage data area may store data created according to use of a processing device operated by the server, and the like. Further, the memory 42 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 42 may optionally include memory located remotely from processor 41, which may be connected to a network connection device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 43 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the processing device of the server. The output device 44 may include a display device such as a display screen.
One or more modules are stored in the memory 42, which when executed by the one or more processors 41, perform the method as shown in fig. 1.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the motor control methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a Random Access Memory (RAM), a flash memory (FlashMemory), a hard disk (hard disk drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present disclosure have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present disclosure, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. An information processing method for insurance business, comprising:
acquiring flow user behavior data;
analyzing the flow user behavior data in a preset dimension to obtain analyzed data;
matching the analyzed data with preset data in a preset data set to determine matched data matched with the analyzed data in the preset data set;
determining the label mapped with the matching data as a label of the traffic user behavior data;
and combining the labels of the traffic user behavior data according to a preset mode to obtain a user portrait.
2. The information processing method for insurance services according to claim 1, wherein analyzing the traffic user behavior data in a preset dimension to obtain analyzed data comprises:
performing data extraction on the traffic user behavior data according to a preset dimension to obtain extracted data;
based on the extracted data, generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy;
and determining a rule data set formed by the rule data as analyzed data.
3. The information processing method for insurance services according to claim 1 or 2, wherein the matching the parsed data with preset data in a preset data set, and the determining the matching data in the preset data set that matches the parsed data comprises:
matching the analyzed data with preset data in a preset data set, and determining preset data which are the same as the analyzed data;
and determining the preset data which is the same as the analyzed data as matching data.
4. The information processing method for insurance services according to claim 1, wherein said determining the label mapped with said matching data as a label of traffic user behavior data comprises:
acquiring a pre-established label set, wherein labels in the label set are associated with at least one piece of preset data;
and determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
5. The information processing method for insurance services according to claim 1, wherein said obtaining traffic user behavior data comprises:
acquiring flow user behavior data from a preset position;
storing the traffic user behavior data in a database;
and acquiring the flow user behavior data from a database.
6. An information processing apparatus for insurance services, comprising:
the acquiring unit acquires flow user behavior data;
the analysis unit is used for analyzing the preset dimensionality of the flow user behavior data to obtain analyzed data;
the matching unit is used for matching the analyzed data with preset data in a preset data set so as to determine matching data matched with the analyzed data in the preset data set;
the determining unit is used for determining the label mapped with the matching data as a label of the traffic user behavior data;
and the combination unit is used for combining the labels of the traffic user behavior data according to a preset mode to obtain a user portrait.
7. The information processing apparatus for insurance service according to claim 6, wherein the parsing unit includes:
the extraction module is used for extracting the data of the flow user behavior data according to a preset dimension to obtain extracted data;
the generating module is used for generating rule data of the granular flow user behavior data corresponding to the extracted data by using a preset strategy based on the extracted data;
and the determining module is used for determining a rule data set formed by the rule data as the analyzed data.
8. The information processing apparatus for insurance service according to claim 6 or 7, wherein the matching unit includes:
the matching module is used for matching the analyzed data with preset data in a preset data set and determining the preset data which is the same as the analyzed data;
and the first determining module is used for determining the preset data which is the same as the analyzed data as matching data.
9. The information processing apparatus for insurance service according to claim 6, wherein the determination unit includes:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module acquires a pre-established label set, and labels in the label set are associated with at least one piece of preset data;
and the second determination module is used for determining the label associated with the matching data in the label set as the label of the traffic user behavior data.
10. The information processing apparatus for insurance service according to claim 6, wherein the acquisition unit includes:
the first acquisition module is used for acquiring flow user behavior data from a preset position;
the storage module is used for storing the flow user behavior data into a database;
and the second acquisition module is used for acquiring the flow user behavior data from a database.
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