CN111967904A - User data processing method and device, computer storage medium and electronic equipment - Google Patents
User data processing method and device, computer storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to the field of computers, and provides a user data processing method, an apparatus, a computer storage medium and an electronic device, wherein the method comprises: acquiring buried point data generated by a user terminal aiming at an agent identifier, wherein the buried point data comprises user behavior data which is generated by a user on the user terminal and corresponds to the agent identifier; and determining a target agent identification according to the user behavior data and a preset rule, binding the user with the target agent identification, and sending target buried point data corresponding to the target agent identification to an agent end corresponding to the target agent identification. The method and the system can bind the target agent according to the user behavior data generated by the user, and effectively improve the reasonable distribution of user resources.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a user data processing method, a user data processing apparatus, a computer-readable storage medium, and an electronic device.
Background
With the development of computer technology, the method for acquiring user information in the sales industry has not been limited to offline forms, such as offline activity guidance, banner advertisement, field announcement, etc., and more tools and channels such as online application programs, applets, public numbers, websites or external links are used to acquire user information.
However, the existing manner of acquiring user resources often causes a situation that one or more agents commonly own the same user resources, which results in that the agents contend for the user resources on one hand and is not beneficial to the agents accurately identify the users on the other hand; on the other hand, the user is not easy to learn about the product.
In view of the above, there is a need in the art to develop a new user data processing method and apparatus to solve the above-mentioned problem of user resource allocation.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a user data processing method, a user data processing apparatus, a computer-readable storage medium, and an electronic device, so as to improve the reasonable allocation of user resources at least to a certain extent.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, there is provided a user data processing method, the method including: acquiring buried point data corresponding to an agent identifier on a user terminal, wherein the buried point data comprises user behavior data which is generated on the user terminal by a user and corresponds to the agent identifier; and determining a target agent according to the user behavior data and a preset rule, binding the user and the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent.
In some exemplary embodiments of the present disclosure, the buried point data comprises one or more agent identifications and one or more sets of user behavior data corresponding to each of the agent identifications, each of the user behavior data comprising a user behavior identification; determining a target agent according to the user behavior data and a preset rule, wherein the step of determining the target agent comprises the following steps: if the buried point data only comprises one agent identification, taking an agent corresponding to the agent identification as the target agent; and if the buried point data comprises a plurality of agent identifications, acquiring user behavior identifications corresponding to the agent identifications, and determining the target agent according to the user behavior identifications corresponding to the agent identifications.
In some exemplary embodiments of the present disclosure, determining the target agent according to the user behavior identifier corresponding to each agent identifier includes: if the number of the user behavior identifications corresponding to each agent identification is one, acquiring the priority of each user behavior identification; and determining the agent identification corresponding to the user behavior identification with the highest priority as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
In some exemplary embodiments of the present disclosure, determining the target agent according to the user behavior identifier corresponding to each agent identifier includes: acquiring a weight value corresponding to the user behavior identifier, and calculating a total weight value corresponding to each agent identifier according to the weight value; and determining the agent identification corresponding to the maximum total weight value as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
In some exemplary embodiments of the present disclosure, the buried point data further comprises a user identification; binding the user with the target agent, comprising: and binding the user and the target agent according to the user identification and the target agent identification corresponding to the target agent.
In some exemplary embodiments of the present disclosure, sending the target buried point data corresponding to the target agent identifier to an agent corresponding to the target agent identifier includes: and acquiring the target agent identification corresponding to the user identification according to the user identification, acquiring target buried point data corresponding to the target agent identification from the buried point data, and sending the target buried point data to an agent end corresponding to the target agent identification.
In some exemplary embodiments of the present disclosure, the method further comprises: at the Nth moment, acquiring first buried point data on the user side, determining that the target agent is a first agent according to the first buried point data, and binding the user and the first agent; at the (N + 1) th moment, acquiring second buried point data on the user side, and determining that the target agent is a second agent according to the second buried point data; when the first agent is different from the second agent, unbinding the user from the first agent, and binding the user with the second agent; and N is a positive integer, and the second buried point data comprises the first buried point data and new buried point data generated by the user at the (N + 1) th moment.
According to an aspect of the present disclosure, there is provided a user data processing apparatus including: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring buried point data corresponding to an agent identifier on a user terminal, and the buried point data comprises user behavior data which is generated on the user terminal by a user and corresponds to the agent identifier; and the distribution module is used for determining a target agent according to the user behavior data and preset rules, binding the user with the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent.
According to an aspect of the present disclosure, there is provided a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the user data processing method as described in the above embodiments.
According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the user data processing method as described in the above embodiments.
As can be seen from the foregoing technical solutions, the user data processing method and apparatus, the computer-readable storage medium, and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
the user data processing method in the exemplary embodiment of the present disclosure may obtain buried point data corresponding to the agent identifier on the user terminal, where the buried point data includes user behavior data corresponding to the agent identifier, which is generated on the user terminal by the user; and determining a target agent according to the user behavior data and a preset rule, binding the user and the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent. According to the user data processing method, on one hand, the target buried point data can be sent to the agent end corresponding to the target agent, the mutual exclusion between the data corresponding to each agent in the buried point data is mined by utilizing the advantages of the buried point data, the data corresponding to each agent are subjected to data isolation, the target agent is facilitated to effectively hold user resources, and further the business transaction rate of the target agent and the user is improved; on the other hand, the target agent can be determined according to the user behavior data, and the user and the target agent are bound, so that the reasonable distribution of user resources is effectively improved, and the phenomenon that the agent contends for and robbes the user resources is avoided.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically shows an architectural diagram of a user data processing system according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow diagram of a user data processing method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow diagram for determining a target agent according to a preset rule according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram for determining a target agent according to an embodiment of the present disclosure;
FIG. 5 schematically shows a flow diagram of user data processing according to an embodiment of the present disclosure;
FIG. 6 schematically shows a block diagram of a user data processing apparatus according to an embodiment of the present disclosure;
FIG. 7 schematically shows a block schematic of an electronic device according to an embodiment of the disclosure;
fig. 8 schematically shows a program product schematic according to an embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
FIG. 1 shows a block diagram of a user data processing system to which embodiments of the present disclosure may be applied.
As shown in FIG. 1, user data processing system 100 may include a user side 110, a server side 120, and a proxy side 130. The server 120 can obtain user data generated by operations of the user on the user side 110, process the user data, and send the processed user data to the agent side 130; the server 120 also obtains the proxy data or service data generated by the agent operating on the agent 130, and sends the proxy data or service data to the client 110. The user terminal 110 and the agent terminal 130 may be in the form of application software, applet, public number, website, or external link, and the disclosure is not limited thereto.
In the prior art, an agent shows or propagates products through an agent terminal 130, and a user connects the agent through a user terminal 110, but a situation that one or more agents serve one user together often occurs, which on one hand causes the agent to contend for user resources and is not beneficial to the agent to accurately identify the user; on the other hand, the user is not easy to learn about the product.
Based on the problems in the related art, in one embodiment of the present disclosure, a user data processing method is provided, which can be applied to, but is not limited to, the following scenarios: the method comprises the following steps of (1) the field of insurance sales, the field of house property sales, the field of aged subject acquisition, the field of training subjects and the like, wherein the specific application scene of the user data processing method is not particularly limited by the disclosure, and the change of the specific scene is understood to belong to the protection scope of the disclosure. Fig. 2 shows a flow diagram of a user data processing method, which, as shown in fig. 2, comprises at least the following steps:
step S210: acquiring buried point data corresponding to the agent identifier on the user side 110, wherein the buried point data comprises user behavior data which is generated on the user side 110 by a user and corresponds to the agent identifier;
step S220: and determining a target agent according to the user behavior data and preset rules, binding the user with the target agent, and sending target buried point data corresponding to the target agent to the agent terminal 130 corresponding to the target agent.
On one hand, the user data processing method in the embodiment of the disclosure can send the target buried point data to the agent end 130 corresponding to the target agent, and utilizes the advantages of the buried point data to mine the mutual exclusivity between the data corresponding to each agent in the buried point data, and perform data isolation on the data corresponding to each agent, thereby being beneficial to the target agent to effectively hold user resources and further improving the business transaction rate of the target agent and the user; on the other hand, the target agent can be determined according to the user behavior data, and the user and the target agent are bound, so that the reasonable distribution of user resources is effectively improved, and the phenomenon that the agent contends for and robbes the user resources is avoided.
It should be noted that the user data processing method according to the exemplary embodiment of the present disclosure may be executed by a server, and a user data processing apparatus corresponding to the user data processing method may also be configured in the server. Furthermore, it should be understood that a terminal device (e.g., a mobile phone, a tablet, etc.) may also implement the steps of the user data processing method, and a corresponding user data processing apparatus may also be configured in the terminal device.
In order to make the technical solution of the present disclosure clearer, each step of the user data processing method is explained next.
In step S210, buried point data corresponding to the agent identifier on the user terminal 110 is obtained, where the buried point data includes user behavior data corresponding to the agent identifier generated by the user on the user terminal 110.
In an exemplary embodiment of the disclosure, the buried point data may include a user identifier and one or more agent identifiers, and may further include one or more sets of user behavior data corresponding to each agent identifier, and each user behavior data may include a user behavior identifier corresponding to a user behavior generated by a user at the user terminal 110, and may further include a timestamp for generating the user behavior, and the like, which is not specifically limited by the disclosure.
The user identifier may be a name of the user, a contact information of the user, an identity number of the user, or a login nickname or a login account number when the user logs in the user terminal 110, which is not specifically limited by the present disclosure. The agent identifier may be an agent name, an agent contact address, an agent identification number, an agent job number, a name card identifier corresponding to an agent name card, or a login nickname or a login account number when the agent logs in the agent terminal 130, which is not specifically limited in this disclosure.
The agent name card may include an agent name, an agent contact mode, head portrait information of the agent, enterprise information of the agent, mall information shared by the agent, articles and video information recommended by the agent, a name card sharing button, a contact right button, and the like, which is not limited in this disclosure.
For example, the server 120 records all user behavior traces of the user on the client 110 while the user browses the client 110, for example, when the user enters the user data processing system 100 through sharing by which agent, which article and/or which video the user browses in the client 110, which user clicks on which agent's homepage in the client 110, which button is clicked on the agent's homepage, and the like, which is not specifically limited by the disclosure. The data of the user on the user terminal 110 is formed by mining the user behavior track and browsing records left by the user in the user terminal 110.
In an exemplary embodiment of the present disclosure, different user behaviors generated by the user at the user terminal 110 correspond to different user behavior identifiers, and the different user behaviors have different priorities and weighted values according to the possibility of reaching a deal by a behavior touch, and the user behavior, the user behavior identifier, the priority corresponding to the user behavior, and the weighted value corresponding to the user behavior may be stored in the database in a one-to-one correspondence manner.
For example, the user clicks the user behavior identifier 01 corresponding to the "contact on horse" on the agent business card; a user clicks a user behavior identifier corresponding to a 'shared name card' on a proxy name card to be 02; the user clicks the mall page on the agent name card to browse the user behavior mark 03 corresponding to the mall page; and clicking the proxy name card by the user to check that the user behavior identifier corresponding to the home page is 04. The priority ranking corresponding to the user behavior identifier may be 01>02>03>04, the weight value ranking corresponding to the user behavior identifier may also be 01>02>03>04, and the priority corresponding to the user behavior identifier and the weight value corresponding to the user behavior identifier may be in a direct proportion relationship. Of course, the priority and the weight value corresponding to the user behavior identifier may also be in a direct relationship, an inverse relationship, or no relationship, and this disclosure does not specifically limit this.
In step S220, a target agent is determined according to the user behavior data and a preset rule, the user and the target agent are bound, and the target buried point data corresponding to the target agent is sent to the agent 130 corresponding to the target agent.
In an exemplary embodiment of the present disclosure, the preset rule may be set according to an actual situation, for example, the target agent may be determined according to a priority of a user behavior in the user behavior data, or the target agent may be determined by performing weighted summation according to each user behavior in the user behavior data, which is not specifically limited by the present disclosure.
For example, fig. 3 is a schematic diagram illustrating a process of determining a target agent according to a preset rule, as shown in fig. 3, the process at least includes steps S310 to S330, which are described in detail as follows:
in step S310, the number of agent identifications included in the buried point data is acquired.
In an exemplary embodiment of the present disclosure, the agent identifier included in the buried point data is acquired, and there is a case where only one agent identifier or a plurality of agent identifiers are included in the buried point data. Therefore, the number of the agent identifications included in the buried point data is obtained first, and the target agent is determined according to the number of the agent identifications included in the buried point data.
In step S320, if only one agent identifier is included in the buried point data, the agent corresponding to the agent identifier is used as the target agent.
In the exemplary embodiment of the present disclosure, the buried point data includes only one agent identifier, which indicates that the user has a business connection only with the agent corresponding to the agent identifier on the user side 110, and then the agent corresponding to the agent identifier is taken as the target agent. The target agent is bound with the user, and the buried point data generated by the user on the user terminal 110 is sent to the agent terminal 130 corresponding to the target agent, so that the agent can check the buried point data through the agent terminal 130, further know the intention of the user, and obtain further contact with the user.
For example, when the user logs in the user terminal 110 for the first time, the server terminal 120 may randomly push an agent business card of an agent to the user terminal 110, and if the user clicks the agent business card on the user terminal 110, a piece of buried point data corresponding to the agent business card is generated on the user terminal 110. The server 120 obtains the buried point data, determines the agent as a target agent, binds the target agent with the user, and pushes the buried point data of the user to the agent terminal 130 corresponding to the target agent.
In addition, when the user logs in the user terminal 110 for the first time, the server terminal 120 may also push agent name cards of a plurality of agents to the user terminal 110, the user browses the agent name cards on the user terminal 110, and selects to click to enter one of the first agent name cards, a piece of buried point data corresponding to the first agent name card is generated on the user terminal 110, and the server terminal 120 obtains the buried point data to determine the first agent as the target agent.
In addition, when the user clicks to enter the user terminal 110 through the link shared by the agent or the two-dimensional code, a piece of buried point data corresponding to the agent is generated on the user terminal 110, and the server terminal 120 obtains the buried point data to determine the agent as the target agent.
In step S330, if the buried point data includes a plurality of agent identifiers, a user behavior identifier corresponding to each agent identifier is obtained, and a target agent is determined according to the user behavior identifier corresponding to each agent identifier.
In an exemplary embodiment of the present disclosure, the buried point data includes a plurality of agent identifications, which indicate that the user has business contact with the plurality of agents, and then a target agent is determined among the plurality of agents. And if the number of the user behavior identifications corresponding to each agent identification is one, determining a target agent according to the priority of the user behavior identification corresponding to each agent identification.
Specifically, if the number of the user behavior identifiers corresponding to each agent identifier is one, the priority of each user behavior identifier is obtained; and determining the agent identification corresponding to the user behavior identification with the highest priority as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
For example, the buried point data includes: the user behavior identifier corresponding to the agent identifier a is 02, the user behavior identifier corresponding to the agent identifier b is 03, and the user behavior identifier corresponding to the agent identifier c is 01. And if the priority ranking of the obtained user behavior identifiers is 03>02>01, the target agent is identified as 03, and the agent B is determined as the target agent.
In an exemplary embodiment of the present disclosure, a weight value corresponding to a user behavior identifier is obtained, and a total weight value corresponding to each agent identifier is calculated according to the weight value; and determining the agent identification corresponding to the maximum total weight value as a target agent identification, and taking the agent corresponding to the target agent identification as a target agent.
Specifically, when the number of the user behavior identifiers corresponding to each agent identifier is multiple, fig. 4 shows a schematic flow chart of determining the target agent, and as shown in fig. 4, in step S410, weight values corresponding to multiple user behavior identifiers corresponding to each agent identifier are obtained; in step S420, summing the weight values corresponding to the plurality of user behavior identifiers to obtain a total weight value corresponding to each agent identifier; in step S430, the agent corresponding to the agent identifier having the largest total weight value is determined as the target agent.
In addition, when the number of the user behavior identifiers corresponding to each agent identifier is one, the target agent can also be determined according to the weight value corresponding to each user behavior identifier.
Specifically, a weight value corresponding to a user behavior identifier corresponding to each agent identifier is obtained, the agent identifier corresponding to the maximum weight value is determined as a target agent identifier, and an agent corresponding to the target agent identifier is used as a target agent.
In an exemplary embodiment of the present disclosure, binding a user with a target agent includes: and binding the user and the target agent according to the user identification and the target agent identification corresponding to the target agent.
Specifically, a binding relationship table between a user and a target agent may be established in a database, where the binding relationship table includes a user identifier and a target agent identifier, and in the binding relationship table, the user identifier and the target agent identifier have a corresponding relationship, each user identifier corresponds to one target agent identifier, and a target agent identifier corresponding to the user identifier may be found through the user identifier, but one agent identifier may correspond to only one user identifier or to multiple user identifiers, which is not specifically limited in this disclosure.
In addition, the binding between the user and the target agent can also be obtained by binding the user end ID corresponding to the user identifier and the agent end ID corresponding to the target agent identifier.
In an exemplary embodiment of the present disclosure, sending the target buried point data corresponding to the target agent identifier to the agent 130 corresponding to the target agent identifier includes: and acquiring a target agent identifier corresponding to the user identifier according to the user identifier, acquiring target buried point data corresponding to the target agent identifier from the buried point data, and sending the target buried point data to the agent terminal 130 corresponding to the target agent identifier.
Specifically, when the target buried point data is sent to the agent 130 corresponding to the target agent identifier, the user behavior corresponding to the target buried point data may be counted, and the agent 130 may be updated according to the target buried point data.
The agent terminal 130 may include a data statistics page, where the data statistics page may include the number of users having a binding relationship with the agent, the number of times that a plurality of users browse agent business cards, the number of times that a plurality of users contact the agent, and the like, that is, the number of user behavior data generated by the users for the agent identifier may be displayed, and each user behavior identifier may correspond to one data statistics entry, which is not specifically limited by the present disclosure.
In addition, in addition to displaying the number of times of browsing the agent business card and the number of times of contacting with the agent, specific user behavior information corresponding to the number of times may also be displayed, for example, time corresponding to each user behavior may be displayed, and the disclosure does not specifically limit this.
For example, if the target agent is bound to 6 users, the data statistics page at the agent end 130 may display that the target agent has 6 users, may also display basic information of the 6 users, such as user names, user contact information, and the like, and may also display the number of user behaviors that the user generates "contact immediately", which is not specifically limited by the present disclosure. When receiving a piece of target buried point data, the agent 130 updates the data statistics page in the agent 130 according to the target buried point data.
Of course, the updating of the data statistics page in the proxy 130 according to the target buried point data may also be completed at the server 120, which is not specifically limited in this disclosure.
In the exemplary embodiment of the present disclosure, since the buried point data corresponding to the user terminal 110 may change in real time, the server terminal 120 may obtain the buried point data on the user terminal 110 in real time, and the buried point data obtained at each time may be the same or different, and therefore, the target agent determined according to the buried point data at different times may be the same agent or different agents.
Specifically, fig. 5 shows a schematic flow chart of user data processing, and as shown in fig. 5, the flow chart at least includes step S510 to step S530, which are described in detail as follows:
in step S510, at the nth time, the first embedded point data on the user terminal 110 is obtained, and the target agent is determined to be the first agent according to the first embedded point data, and then the user is bound with the first agent.
In the exemplary embodiment of the present disclosure, the method for obtaining the first embedding point data, determining the first agent according to the first embedding point data, and binding the user and the first agent is described in detail in the above embodiment, and details are not described here again. Where N is a positive integer, that is, each time may be an nth time, and the nth +1 th time is a next time of the nth time.
In step S520, at the N +1 th time, second buried point data on the user terminal 110 is obtained, and the target agent is determined to be the second agent according to the second buried point data.
In the exemplary embodiment of the present disclosure, the second embedding point data is obtained, and it is determined that the second agent has been described in detail in the above embodiment according to the second embedding point data, and details are not described here.
And the second buried point data comprises the first buried point data and new buried point data generated by the user at the (N + 1) th moment.
In step S530, when the first agent is different from the second agent, the user and the first agent are unbound, and the user and the second agent are bound.
In an exemplary embodiment of the present disclosure, whether the first agent is the same as the second agent is determined, which may be determined according to the agent identifier, and if the agent identifier corresponding to the first agent is different from the agent identifier corresponding to the second agent, the first agent is different from the second agent; and if the agent identification corresponding to the first agent is the same as the agent identification corresponding to the second agent, the first agent is the same as the second agent. Of course, the method for determining whether the first agent is the same as the second agent may be other determination methods, and this disclosure does not specifically limit this.
In the exemplary embodiment of the disclosure, if the first agent is different from the second agent, it indicates that the user generates new user behavior data for the agent identifier corresponding to the second agent at the time N +1, and it is determined that the business connection between the user and the second agent is closer according to the new user behavior data and the historical user behavior data. Thus, the user may be unbound from the first agent and bound to the second agent.
In addition, when the user and the first agent are unbound, the target buried point data related to the user on the agent end 130 corresponding to the first agent can be deleted; when the user is bound with the second agent, the target buried point data corresponding to the second agent in the second buried point data may be further sent to the agent 130 corresponding to the second agent.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. The computer program, when executed by the CPU, performs the functions defined by the method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following describes embodiments of the apparatus of the present disclosure, which may be used to perform the above-mentioned user data processing method of the present disclosure. For details that are not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the user data processing method described above in the present disclosure.
Fig. 6 schematically shows a block diagram of a user data processing device according to an embodiment of the present disclosure.
Referring to fig. 6, a user data processing apparatus 600 according to an embodiment of the present disclosure, the user data processing apparatus 600 includes: the data acquisition module 601 and the data processing module 602 specifically:
a data obtaining module 601, configured to obtain buried point data corresponding to the agent identifier on the user side 110, where the buried point data includes user behavior data corresponding to the agent identifier, and the user behavior data is generated on the user side 110 by a user;
the data processing module 602 is configured to determine a target agent according to the user behavior data and a preset rule, bind the user with the target agent, and send target buried point data corresponding to the target agent to the agent 130 corresponding to the target agent.
In an exemplary embodiment of the present disclosure, the data processing module 602 may further be configured to determine the target agent according to a preset rule according to the user behavior data, including: if the buried point data only comprises one agent identification, taking an agent corresponding to the agent identification as a target agent; if the buried point data comprises a plurality of agent identifications, acquiring user behavior identifications corresponding to the agent identifications, and determining a target agent according to the user behavior identifications corresponding to the agent identifications; the buried point data comprises one or more agent identifications and one or more groups of user behavior data corresponding to the agent identifications, and each group of user behavior data comprises a user behavior identification.
In an exemplary embodiment of the present disclosure, the data processing module 602 may further be configured to determine the target agent according to the user behavior identifier corresponding to each agent identifier, including: if the number of the user behavior identifications corresponding to each agent identification is one, acquiring the priority of each user behavior identification; and determining the agent identification corresponding to the user behavior identification with the highest priority as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
In an exemplary embodiment of the present disclosure, the data processing module 602 may further be configured to determine the target agent according to the user behavior identifier corresponding to each agent identifier, including: acquiring a weight value corresponding to the user behavior identifier, and calculating a total weight value corresponding to each agent identifier according to the weight value; and determining the agent identification corresponding to the maximum total weight value as a target agent identification, and taking the agent corresponding to the target agent identification as a target agent.
In an exemplary embodiment of the present disclosure, the user data processing apparatus 600 may further include a loop update module (not shown in the figure), and the loop update module may be further configured to, at the nth time, obtain first embedded data on the user terminal 110, determine that the target agent is the first agent according to the first embedded data, and then bind the user with the first agent; at the (N + 1) th moment, acquiring second buried point data on the user terminal 110, and determining that the target agent is a second agent according to the second buried point data; when the first agent is different from the second agent, the user and the first agent are unbound, and the user and the second agent are bound; and N is a positive integer, and the second buried point data comprises the first buried point data and new buried point data generated by the user at the (N + 1) th moment.
The details of the user data processing apparatuses are already described in detail in the corresponding user data processing methods, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the apparatus for performing are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the storage unit stores program code that is executable by the processing unit 710 such that the processing unit 710 performs the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 710 may execute step S210 shown in fig. 2, to obtain buried point data corresponding to the agent identifier on the user terminal 110, where the buried point data includes user behavior data corresponding to the agent identifier generated by the user on the user terminal 110; and step S220, determining a target agent according to the user behavior data and preset rules, binding the user with the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent.
The storage unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 700 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a viewer to interact with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 8, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (10)
1. A method for processing user data, comprising:
acquiring buried point data corresponding to an agent identifier on a user terminal, wherein the buried point data comprises user behavior data which is generated on the user terminal by a user and corresponds to the agent identifier;
and determining a target agent according to the user behavior data and a preset rule, binding the user and the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent.
2. The user data processing method of claim 1, wherein the buried point data comprises one or more agent identifications and one or more sets of user behavior data corresponding to each of the agent identifications, each of the user behavior data comprising a user behavior identification;
determining a target agent according to the user behavior data and a preset rule, wherein the step of determining the target agent comprises the following steps:
if the buried point data only comprises one agent identification, taking an agent corresponding to the agent identification as the target agent;
and if the buried point data comprises a plurality of agent identifications, acquiring user behavior identifications corresponding to the agent identifications, and determining the target agent according to the user behavior identifications corresponding to the agent identifications.
3. The user data processing method of claim 2, wherein determining the target agent according to the user behavior identifier corresponding to each agent identifier comprises:
if the number of the user behavior identifications corresponding to each agent identification is one, acquiring the priority of each user behavior identification;
and determining the agent identification corresponding to the user behavior identification with the highest priority as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
4. The user data processing method of claim 2, wherein determining the target agent according to the user behavior identifier corresponding to each agent identifier comprises:
acquiring a weight value corresponding to the user behavior identifier, and calculating a total weight value corresponding to each agent identifier according to the weight value;
and determining the agent identification corresponding to the maximum total weight value as the target agent identification, and taking the agent corresponding to the target agent identification as the target agent.
5. The user data processing method of claim 1, wherein the buried point data further comprises a user identification;
binding the user with the target agent, comprising:
and binding the user and the target agent according to the user identification and the target agent identification corresponding to the target agent.
6. The user data processing method of claim 5, wherein sending the target buried point data corresponding to the target agent identifier to an agent corresponding to the target agent identifier comprises:
and acquiring the target agent identification corresponding to the user identification according to the user identification, acquiring target buried point data corresponding to the target agent identification from the buried point data, and sending the target buried point data to an agent end corresponding to the target agent identification.
7. The user data processing method according to any of claims 1 to 6, characterized in that the method further comprises:
at the Nth moment, acquiring first buried point data on the user side, determining that the target agent is a first agent according to the first buried point data, and binding the user and the first agent;
at the (N + 1) th moment, acquiring second buried point data on the user side, and determining that the target agent is a second agent according to the second buried point data;
when the first agent is different from the second agent, unbinding the user from the first agent, and binding the user with the second agent;
and N is a positive integer, and the second buried point data comprises the first buried point data and new buried point data generated by the user at the (N + 1) th moment.
8. A user data processing apparatus, comprising:
the data acquisition module is used for acquiring buried point data corresponding to the agent identification on a user terminal, wherein the buried point data comprises user behavior data which is generated on the user terminal by a user and corresponds to the agent identification;
and the data processing module is used for determining a target agent according to the user behavior data and preset rules, binding the user with the target agent, and sending target buried point data corresponding to the target agent to an agent end corresponding to the target agent.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a user data processing method according to any one of claims 1 to 7.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a user data processing method according to any one of claims 1 to 7.
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