CN114257842B - Praise data processing system, method and device and storage medium - Google Patents

Praise data processing system, method and device and storage medium Download PDF

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CN114257842B
CN114257842B CN202111564950.3A CN202111564950A CN114257842B CN 114257842 B CN114257842 B CN 114257842B CN 202111564950 A CN202111564950 A CN 202111564950A CN 114257842 B CN114257842 B CN 114257842B
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praise
data
data processing
module
processing
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CN114257842A (en
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付光煜
郭晓阳
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Ping An Property and Casualty Insurance Company of China Ltd
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Ping An Property and Casualty Insurance Company of China Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/235—Processing of additional data, e.g. scrambling of additional data or processing content descriptors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/258—Client or end-user data management, e.g. managing client capabilities, user preferences or demographics, processing of multiple end-users preferences to derive collaborative data
    • H04N21/25866—Management of end-user data
    • H04N21/25891—Management of end-user data being end-user preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Computer Graphics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The embodiment of the application provides a praise data processing system, a praise data processing method, a praise data processing device and a storage medium, wherein the praise data processing system comprises: the system comprises a client, a praise data processing device and a praise data analysis device, wherein the praise data processing device receives praise data carrying a module identifier; carrying out data processing on the point-to-point approval data according to the module identification; updating the data record associated with the praise data by using the processing result; receiving a data acquisition request sent by the praise data analysis equipment; acquiring target data from the updated data record, and sending the target data to the praise data analysis equipment; the praise data analysis device performs data analysis according to the target data; transmitting the analysis result to the praise data processing equipment; the praise data processing device recommends information to the client based on the analysis result. The method and the device unify praise data collection processing and analysis and simplify logic processing of each module, and relate to the blockchain technology, such as writing the data into the blockchain for data processing and other scenes.

Description

Praise data processing system, method and device and storage medium
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a system, a method, an apparatus, and a storage medium for processing praise data.
Background
With the rapid development of computer technology, functions of clients become diversified, for example, a plurality of clients may have praise functions, and users may perform corresponding praise operations. In general, the praise operation may involve various scenarios, such as praise operation for a post, or a comment, or a list, etc. At present, the processing of the praise data in different scenes is usually independent development processing, so that repeated development of corresponding functional modules in different scenes can be caused, and resource waste is caused.
Disclosure of Invention
The embodiment of the application provides a praise data processing system, a method, a device and a storage medium, which establish unified praise data collection processing and analysis, and can simplify the logic processing of the praise data of each module included in a client, thereby reducing the maintenance cost of each module.
The first aspect of the embodiment of the application provides a praise data processing system, which comprises a client with a praise function, a praise data processing device and a praise data analysis device, wherein the client is provided with a plurality of praise function sub-modules;
The praise data processing device is used for receiving praise data uploaded by the client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client; performing data processing on the praise data according to the module identifier to obtain a corresponding processing result; updating the data record associated with the praise data by using the processing result;
the praise data processing device is further used for receiving a data acquisition request sent by the praise data analysis device and used for requesting to acquire target data; acquiring target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment;
the praise data analysis equipment is used for receiving the target data and carrying out data analysis according to the target data to obtain a corresponding analysis result; transmitting the analysis result to the praise data processing device;
the praise data processing device is also used for receiving the analysis result sent by the praise analysis device and recommending information to the client based on the analysis result.
A second aspect of the embodiments of the present application provides a praise data processing method, where the praise data processing method is applied to a praise data processing device in a praise data processing system, and the method includes:
receiving praise data uploaded by a client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client;
performing data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by using the processing result;
receiving a data acquisition request sent by the praise data analysis equipment and used for requesting to acquire target data, acquiring the target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment;
and receiving an analysis result sent by the praise analysis equipment, and recommending information to the client based on the analysis result.
A third aspect of the embodiments of the present application provides a praise data processing apparatus, the praise data processing apparatus being disposed in a risk assessment device, the apparatus comprising:
The receiving unit is used for receiving the praise data uploaded by the client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client;
the processing unit is used for carrying out data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by utilizing the processing result;
the receiving unit is further used for receiving a data acquisition request which is sent by the praise data analysis equipment and used for requesting to acquire target data;
the acquisition unit is used for acquiring target data corresponding to the data acquisition request from the updated data record;
a transmitting unit configured to transmit the target data to the praise data analyzing apparatus;
the receiving unit is further used for receiving the analysis result sent by the praise analysis device;
and the recommending unit is used for recommending information to the client based on the analysis result.
In a third aspect, an embodiment of the present application discloses a praise data processing apparatus, including a processor and a memory, where the memory is configured to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to perform the method of the second aspect.
In a fourth aspect, embodiments of the present application disclose a computer readable storage medium storing a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method of the second aspect described above.
In the embodiment of the application, the praise data uploaded by the client can be received, the praise data carries the module identifier of the target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client. And the data processing can be carried out on the praise data according to the module identification so as to obtain a corresponding processing result, and the processing result is utilized to update the data record associated with the praise data. The data acquisition request sent by the praise data analysis device and used for requesting to acquire the target data can be received, so that the target data corresponding to the data acquisition request can be acquired from the updated data record, the target data is sent to the praise data analysis device, the analysis result sent by the praise data analysis device is received, and information recommendation is performed on the client based on the analysis result. Through the implementation method, the data of each praise function module included in the client can be unified to the data processing equipment for processing, unified praise data collection processing is established, the maintenance cost of each praise function module is reduced, and the praise function of each praise function module and the data in each module can be understood and coupled. The unified praise data collection processing can also enable the data corresponding to each praise function module included in the client to be communicated, complete data recording and landing are achieved, and data summarization and subsequent data analysis are facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a praise data processing system according to an embodiment of the present application;
fig. 2 is a flow chart of a method for processing like data according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a user relationship network diagram according to an embodiment of the present application;
fig. 4 is a flow chart of a method for processing like data according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a praise data processing apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a praise data processing apparatus according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In order to realize unified management of the praise data, the embodiment of the application provides a praise data processing method which can be executed by a praise data processing device and has the following general principle: in the embodiment of the application, the praise data uploaded by the client can be received, the praise data carries the module identifier of the target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client. And the data processing can be carried out on the praise data according to the module identification so as to obtain a corresponding processing result, and the processing result is utilized to update the data record associated with the praise data. The data acquisition request sent by the praise data analysis device and used for requesting to acquire the target data can be received, so that the target data corresponding to the data acquisition request can be acquired from the updated data record, the target data is sent to the praise data analysis device, the analysis result sent by the praise data analysis device is received, and information recommendation is performed on the client based on the analysis result. By the method, the data of each praise function module included in the client can be unified to the data processing equipment for processing, unified praise data collection processing is established, the maintenance cost of each praise function module is reduced, and the praise function of each praise function module and the data in each module can be understood and coupled. The unified praise data collection processing can also enable the data corresponding to each praise function module included in the client to be communicated, complete data recording and landing are achieved, and data summarization and subsequent data analysis are facilitated.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a praise data processing system according to an embodiment of the present application, where the praise data processing system includes a client 10, a praise data processing device 11 and a praise data analysis device 12. The client 10 may be a client with a praise function, such as a client a or a client B, etc., and is not limited in this application; the client 10 may be a client having a plurality of praise function sub-modules.
Alternatively, a plurality of praise function sub-modules possessed by the client 10 may be divided in advance. In a specific implementation, division of the praise function sub-modules may be performed according to the sources of various praise data included in the client 10. For example, for differences in data sources, the praise data may be classified as material class praise data, such as user or official posts; praise data of comment classes, such as functional comments, post comments, consultation comments and the like; the like of the list, such as a learning list, a growing list, a film and television list and the like; or other data sources, not to be taken as an example in this application. Based on this, the praise function of the client 10 is divided into praise function sub-modules corresponding to the praise data sources according to the difference of the praise data sources, that is, the multiple praise function sub-modules of the client 10 may include a material sub-module, a comment sub-module and a list sub-module. Wherein, a praise function sub-module corresponds to praise data of a data source. When the praise function sub-modules are divided, finer division can be utilized, for example, a data source main class can be divided, and the comment class can be divided into a function comment class, a post comment class, a consultation comment class and the like, so that the corresponding praise function sub-modules are divided more finely. In the present application, the division of the praise function sub-modules is not particularly limited, and the present application mainly uses the example that the praise function sub-modules include a material sub-module, a comment sub-module and a list sub-module. Module identifiers corresponding to the respective praise function sub-modules may also be configured, and the module identifiers may be used to uniquely indicate the corresponding praise function sub-modules. When the corresponding praise data is issued by each praise function sub-module, the corresponding module identification can be attached, so that the praise data processing equipment can process each praise function sub-module according to the module identification.
And a client 10 for providing the user with a praise function and a praise cancellation function. When a praise operation (e.g., praise or cancel praise for a post, praise or cancel praise for a comment, praise or cancel praise for a list, etc.) of the target user is detected, the client 10 may acquire praise data corresponding to the praise operation for the target user, which may include target user information of the target user (which may be referred to as a praise user, i.e., a user who performs the praise operation), praise users, praise time, praise content, etc. The client 10 is further configured to determine a praise function sub-module where the praise operation is currently performed by the target user, where the praise function sub-module may be any praise function sub-module of the multiple praise function sub-modules, and the praise function sub-module where the praise operation is performed by the target user may be referred to as a target point praise function sub-module. The client 10 may further obtain a module identifier corresponding to the target point approval function sub-module after determining the target point approval function sub-module, and carry the module identifier in the approval data, and send the approval data carrying the module identifier to the approval data processing device 11. Alternatively, each of the praise function sub-modules in the client 10 may send corresponding praise data to the praise data processing apparatus 11 through the data forwarding interface, so that the praise data processing apparatus 11 may receive the praise data corresponding to each of the praise function sub-modules. The data forwarding interface may be integrated through a module interface corresponding to each praise function sub-module, so that the data forwarding interface may forward praise data corresponding to each praise function sub-module in the client 10.
And the praise data processing device 11 is used for receiving the praise data carrying the module identifier and uploaded by the client 10, processing the praise data according to the module identifier to obtain a corresponding processing result, and updating the data record associated with the praise data by using the processing result. The praise data processing apparatus 11 is further configured to receive a data acquisition request for requesting acquisition of target data sent from the praise data analysis apparatus 12, and after receiving the data acquisition request, may acquire target data corresponding to the data acquisition request from the updated data record and return the target data to the praise data analysis apparatus 12, so that the praise data analysis apparatus 12 may perform data analysis based on the target data. And the praise data processing apparatus 11 is also for receiving the analysis result transmitted from the praise data analysis apparatus 12 to make information recommendation to the client 10 based on the analysis result.
And a praise data analysis device 12 for sending a data acquisition request for requesting acquisition of the target data to the praise data processing device 11, and receiving the target data returned from the praise data processing device 11 to perform data analysis according to the target data, thereby obtaining a corresponding analysis result. And after the analysis result is obtained, the analysis result may also be sent to the praise data processing apparatus 11 so that the praise data processing apparatus 11 may make information recommendation to the client 10 based on the analysis result.
The implementation details of the technical solutions of the embodiments of the present application are described in detail below:
referring to fig. 2, fig. 2 is a schematic flow chart of a praise data processing method according to an embodiment of the present application, the praise data processing method is related to a client, a praise data processing device and a praise data analysis device, and the embodiment mainly describes an interaction process among the client, the praise data processing device and the praise data analysis device, and the praise data processing method includes the following steps:
s201: when the client detects the praise operation of the target user, praise data for the praise operation is acquired, and a target point praise function sub-module where the praise operation is located is determined based on the praise data.
The target user may refer to any user, and the praise operation may include praise or praise cancellation, and the present application mainly uses praise as an example for relevant explanation.
In one implementation, when the client detects a praise operation of the target user, praise data corresponding to the praise operation may be acquired. The praise data may include target user information of the target user (e.g., a target user account, etc.), praise events (the praise events may include praise material (i.e., material for which the target user performs a praise operation) or praise comments (i.e., comments for which the target user performs a praise operation) or praise sheets (i.e., sheets for which the target user performs a praise operation)), praise user information of the praise user (e.g., a praise user account, such as a user account of a posting user corresponding to the praise comments or praise material), praise time, etc. After the approval data is acquired, an approval function sub-module where the approval operation of the target user is located (which may be referred to as a target point approval function sub-module) may also be determined based on the approval data. For example, the corresponding target point endorsement function sub-module may be determined based on an endorsement event in the endorsement data. If the praise object is a praise material, the corresponding target point praise function sub-module may be a material sub-module, if the praise object is a praise comment, the corresponding target point praise function sub-module may be a comment sub-module, and if the praise object is a praise list, the corresponding target point praise function sub-module may be a list sub-module.
S202: the client acquires the module identification of the target point approval function sub-module and carries the module identification in approval data.
S203: and the client sends the praise data carrying the module identifier to the praise data processing equipment.
In steps S202 and S203, the client may further obtain a module identifier corresponding to the function module with the target point in favor of the function module. The module identifier corresponding to each praise function sub-module may be preset, that is, the client may store a mapping relationship between the praise function sub-module and the module identifier. The module identifier may be used to uniquely indicate a praise function sub-module, and the module identifier may be a numerical number, a bit value (bit), or other information, which is not limited in this application. After the module identifier of the target point approval function sub-module is acquired, the module identifier can be carried in approval data, and the approval data carrying the module identifier can be sent to the approval data processing equipment.
Alternatively, the mapping relationship between the praise function sub-module and the module identifier may be synchronized to the praise data processing apparatus, so that after the praise data processing apparatus receives the praise data, the praise function sub-module corresponding to the praise data may be determined based on the module identifier carried in the praise data.
S204: and the praise data processing equipment performs data processing on the praise data according to the module identification to obtain a corresponding processing result, and updates the data record associated with the praise data by using the processing result.
In one implementation mode, the praise data processing device can receive the praise data which is sent by the client and carries the module identifier, perform data processing on the praise data according to the module identifier to obtain a corresponding processing result, and update the data record associated with the praise data by using the processing result, so that the follow-up praise data analysis device can acquire the latest data to perform data analysis, and the accuracy of the data analysis is improved.
S205: the praise data analysis device transmits a data acquisition request for requesting acquisition of the target data to the praise data processing device.
In one implementation, the endorsement data analysis device may send a data acquisition request to the endorsement data processing device. Alternatively, the data acquisition request may be a data acquisition request transmitted by the data analyst to the praise data processing apparatus through the praise data analysis apparatus. The data analysis device can provide an analysis operation interface for the data analyst, and the data analyst can execute related operations on the analysis operation interface to initiate a data acquisition request, wherein the data acquired by the data acquisition request can be used for data analysis by the data analysis device. Alternatively, the data acquisition request may be sent periodically
In one implementation, the data acquisition request may also be generated by triggering a data analysis task. For example, a data analysis task may be set, where one or more data acquisition requests may be included, and the target data acquired by each data acquisition request may be different. The trigger condition for transmitting the data acquisition request to the praise data processing apparatus may also be indicated in the data analysis task. For example, the trigger condition may be that the current time has reached a preset time, or the like, which may be preset.
The target data may be, for example, a data record for a specific user, for example, a data record of the specific user in a certain period of time, where the data record may include information about a praise corresponding to the specific user, for example, a praise time, a praise content, a publishing user of the praise, and so on; or the relevant information of the praise comments corresponding to the appointed user, such as praise comment time, praise comment content, a posting user of the praise comments and the like; or the relevant information of the praise list corresponding to the appointed user, such as praise list time, praise list content and the like; or the relevant information of the praise material, the relevant information of the praise comment and the relevant information of the praise list corresponding to the appointed user; etc. For another example, the target data may be related information of all praise comments in a period of time, e.g., the related information may be content of each praise comment, posting users of each praise comment, praise number of each praise comment, etc. For another example, the target data may be related information of the praise list, e.g., the related information may be praise users of each list in all the lists, the praise number of each praise list, etc. In the present application, the target data may be set based on various requirements, and is not particularly limited in the present application.
S206: the praise data processing device acquires target data corresponding to the data acquisition request from the updated data record.
S207: the praise data processing apparatus transmits the target data to the praise data analysis apparatus.
S208: and the praise data analysis equipment performs data analysis according to the target data to obtain a corresponding analysis result.
S209: the praise data analysis device transmits the analysis result to the praise data processing device.
In steps S208 and S209, the praise data analysis device may perform data analysis according to the target data after receiving the target data, and may perform different data analysis for the target data acquired by different praise function sub-modules. For example, as described below:
in one implementation, the plurality of praise function sub-modules may include a materials sub-module, and the target data may include all praise materials of the specified user within a preset time period, that is, materials praised by the specified user, such as posts praised by the specified user, and so on. The praise data analysis device may make a determination of the material recommendation type for the specified user based on the target data after receiving the target data, or update of the user portraits for the specified user, or generalization of the interest features for the specified user, or the like. The corresponding analysis results may be material recommendation types for the specified user, updated user portraits for the specified user, interest features for the specified user, and so forth. The results of these data analyses may be used as information recommendations for the specified user by the subsequent clients, for example, using one or more of material recommendation types, updated user portraits, and interest features.
The preset time period may be preset, for example, may be time a-time B, or other time periods, and the preset time period in the following description may be understood as well.
Alternatively, the data analysis is taken as an example to determine the material recommendation type of the specified user. In a specific implementation, the material type corresponding to each praise material can be determined first, the material type can be divided into education, sports, entertainment, financial and other types, and finer division can be performed under the types, for example, the sports type can be divided based on sports items, such as types of table tennis, badminton, basketball and the like. In the present application, the division of the material types is not particularly limited. After determining all of the material types included in the target data, the number of praise materials for each material type may be counted. Then, determining the material recommendation type of the appointed user according to the number of the praise materials. For example, each material type may be sorted in order of the number of praise materials from large to small to obtain a material type sorting result. And after the material type sorting result is obtained, determining the material type in the top N bits in the material type sorting result as the material recommended type. Wherein, N may be a positive integer, for example, the N value may be a value such as 1 or 3, and the specific value is not limited in the present application. After the praise data analysis device determines the material recommendation type for the specified user, the material recommendation type may be transmitted to the praise data processing device.
Optionally, the data analysis is taken as an example to describe the updating of the user portraits of the specified users. In a specific implementation, the original user portrait of the appointed user can include the interest feature of the appointed user, the interest feature is characterized by various material types, and the user portrait of the appointed user can be updated by using the material type sorting result. If the user portrait is updated by the material type of the first M bits, the material type of the first M bits can be used as the updated user portrait of the appointed user. For example, the interest feature in the original user image of the specified user may include a movie type, a travel type, a food type. And the material types in the first three positions in the material type sorting result are film type, music type and food type respectively, so that the travel type in the user portrait can be updated to be music type.
Optionally, the generalization of the features of interest to a given user is illustrated using data analysis as an example. In a specific implementation, the interest point generalization can be performed through some entity words included in the praise material. If the entity words in the praise materials comprise spicy hot pot, the spicy hot pot can be generalized into Chongqing dishes; for example, the entity words in the praise material comprise young pigeons, and the young pigeons can be generalized into cantonese dishes. And for example, chongqing dishes and Guangdong dishes can be used as the interest characteristics of the appointed user so as to better describe the interest points of the user and promote the diversity of information recommendation.
In one implementation, the multiple praise function sub-modules may include a comment sub-module, and the target data may include all praise comments and corresponding publishing users of each praise comment within a preset time period. The praise data analysis device may perform a posting frequency analysis of posting users (e.g., which posting user comments are relatively high frequency), a user preference analysis (e.g., which posting user comments are relatively liked by the user), and so on. For example, the user preference may be determined based on the number of praise comments of each comment posted by a posting user, and the more praise comments of a posting user, the more praise comments the corresponding posting user likes. In a specific implementation, a plurality of posting users included in all the praise comments can be determined, and the number of praise comments of each posting user for praise comments is counted. And then determining a ranking result of the publishing user based on the number of the praise comments, wherein if the ranking result of the publishing user is obtained by ranking a plurality of publishing users based on the order of the number of the praise comments from large to small. Then, based on the ranking results of the users, the user preference may also be determined, for example, if ranking is more forward in the ranking results of the users, it is indicated that the comments posted by the users are more preferred by the users. And after the praise data analysis device obtains the corresponding published user ordering result, the published user ordering result can be sent to the praise data processing device.
In one implementation, the plurality of praise function sub-modules may include a list sub-module, and the target data may include a plurality of praise lists and a list praise number corresponding to each praise list. The praise data analysis device may perform user preference analysis (e.g., which list comparisons are liked by the user), etc., for example, the user preference may be determined based on the number of list praise corresponding to the praise list. In a specific implementation, the order data analysis device may determine an order type of each order, and count an order type order number under each order type based on an order number corresponding to each order. Further, the ranking result of the list type may be determined based on the number of the list type praise, for example, ranking the list type may be performed based on the order of the number of the list type praise from large to small, so as to obtain the ranking result of the list type. Then, based on the ranking result of the list type, the user preference can be determined, for example, if the ranking is higher in the ranking result of the list type, the list type is indicated to be favored by the user. And when the praise data analysis device obtains the list type ranking result, the list type ranking result can be sent to the praise data processing device.
In one implementation, the target data may acquire data under multiple praise function sub-modules in addition to the data under each praise function sub-module described above. For example, a data record corresponding to each praise function sub-module by the designated user can be obtained, for example, all praise materials, praise comments and praise sheets of the designated user in a preset time period can be obtained. And determining the interest characteristics of the appointed user according to the acquired data records, so that information recommendation can be conveniently carried out according to the interest characteristics. For example, all the praise materials, praise comments and praise list respectively corresponding content types can be determined, wherein the content types can comprise film and television types, time types, food types, travel types and the like. And counting the number of praise under each content type, sequentially sequencing each content type according to the order of the number of praise from big to small to obtain a content type sequencing result, and updating the user portrait of the appointed user or generalizing the interest characteristics of the appointed user according to the content type sequencing result. And when the approval data analysis device obtains the content type sorting result, the updated user portrait and the like, the data can be sent to the approval data processing device so that the approval data processing device can conduct information recommendation based on the data.
In one implementation, the praise data analysis device may also perform analysis of the relationship map based on the target data. For example, a user relationship network graph may be established based on the target data, which may include a plurality of nodes, each of which may be used to characterize a user. The users may include posting users for materials, comments, and sheets, and the like, and may also include users performing praise operations on the materials, comments, and sheets. The users represented by any two connected nodes have an association relation; the connection edge between any two nodes may have a value for displaying the strength of the association relationship, which may be determined by the number of praise operations of the user, and when the number is greater, it indicates that the relationship between the two users is stronger. For example, referring to FIG. 3: the numerical value of the connecting edge between the users A and B is 5, namely the number of times that the users A praise the materials, comments and list published by the users B is 5; the value of the connection edge between the users A pointed by the user B is 7, namely the number of praise operations of the materials, comments and list issued by the user A by the user B is 7. Based on this, the specific implementation of establishing a user relationship network graph based on the target data may include: first, all praise data in a preset time period can be acquired, then all node users are determined from the praise data, and the praise times among the node users are determined according to the praise data, so that a user relationship network diagram is constructed according to the praise times among all the node users and the node users. After the endorsement data analysis device determines the user relationship network diagram, the user relationship network diagram can also be sent to the endorsement data processing device, so that the subsequent endorsement data processing device can conduct information recommendation based on the user relationship network diagram.
S210: the praise data processing device recommends information to the client based on the analysis result.
In one implementation, the praise data processing device may receive the analysis results sent by the praise analysis device to make information recommendations to the client based on the analysis results.
In one implementation, as described in step S209, the analysis results may include material recommendation types for the specified user, or updated user portraits for the specified user, or interest features for the specified user, and so forth. The results of these data analyses may be used as information recommendations for the specified user by the subsequent clients, such as by using one or more of material recommendation types, updated user portraits, and interest features. For example, materials related to the material recommendation type can be pushed to a designated user, so that the browsing amount of the user can be improved.
In one implementation, as described in step S209, the analysis result may include a posting user ranking result, and the praise data processing apparatus may determine a comment recommendation sequence of the posting user according to the posting user ranking result, where the comment recommendation sequence may be understood as a display sequence of each comment on the comment interface of the client, that is, may perform comment display on the comment interface of the client according to the comment recommendation sequence. If the ranking of a posting user in the posting user ranking result is more advanced, the comment of the posting user is displayed on the comment interface more advanced. For example, when displaying multiple comments (such as multiple comments for one post), the posting user of each comment may be determined first, whether the posting users exist in the posting user ranking result is determined, if some or all posting users exist in the posting user ranking result, the ranking positions of the posting users in the posting user ranking result are determined, so that comments corresponding to the posting users are sequentially displayed on the comment interface based on the ranking order of the ranking positions of the posting users.
In one implementation, as described in step S209, the analysis result may include a list type ranking result, and the praise data processing device may determine a list recommendation order of a list type according to the list type ranking result, where the list recommendation order may be understood as a display order of each list on a list interface of the client, that is, may perform list display on the list interface of the client according to the list recommendation order. If the ranking of the list type corresponding to a list in the list type ranking result is more advanced, the list is displayed on the list interface more advanced. For example, when displaying multiple sheets, the type of each sheet may be determined first, and whether the types of sheets exist in the ranking result of the types of sheets is determined, if some or all types of sheets exist in the ranking result of the types of sheets, ranking positions of the types of sheets in the ranking result of the types of sheets are determined, so that sheets corresponding to the types of sheets are sequentially displayed on the sheet interface based on the ranking order of the ranking positions of the types of sheets.
In one implementation, as described in step S209, the analysis result may include a user relationship network diagram, where when there is a display requirement for the praise user (the user performing the praise operation), for example, when the praise user corresponding to a certain material needs to view the praise user corresponding to the material, or when the praise user corresponding to a certain comment needs to view the praise user corresponding to the comment, etc., it may be determined that there is a display requirement for the praise user. The display requirement can carry a requirement user and a requirement event of the requirement user, the requirement user can refer to a publishing user, and the requirement event can refer to materials or comments and the like published by the publishing user. Then, after determining that there is a display demand for the praise user, all praise users for the demand event may be further determined from the data record based on the demand user and the demand event for the demand user. And then, determining the number of praise times between the demand user and each praise user from the user relation network diagram, sequencing all the praise users according to the order of the number of praise times from large to small to obtain a praise user sequencing result, and displaying the praise users on the client according to the praise user sequencing result. The user display can be performed according to the relation strength between the users.
Optionally, after each time the praise data processing device receives the praise data uploaded by the client, the user relationship network diagram may be updated according to the received praise data. For example, the praise user and the praise user may be determined from the praise data, then, the praise user and the praise user respectively correspond to the nodes from the user relationship network diagram, and the number of praise times on the connection edge between the two nodes is adjusted to obtain the updated user relationship network diagram.
Through the implementation method, the data of each praise function module included in the client can be unified to the data processing equipment for processing, unified praise data collection processing is established, unified praise data falls to the ground and unified external service is established, the maintenance cost of each praise function module is reduced, the praise function of each praise function module and the data in each module can be understood and coupled, and each praise function module can pay attention to the processing of the praise data without paying attention to the use of the data. The unified praise data collection processing can also enable the data corresponding to each praise function module included in the client to be communicated, complete data recording and landing are achieved, data summarization and subsequent data analysis are facilitated, and analysis of user behaviors is facilitated.
Referring to fig. 4, fig. 4 is a flow chart of a method for processing like data according to an embodiment of the present application. The praise data processing method described in the present embodiment, which is applied to the above-described praise data processing apparatus, includes:
s401: and receiving the praise data uploaded by the target point praise function sub-module, wherein the praise data carries the module identification of the target point praise function sub-module.
S402: and carrying out data processing on the praise data according to the module identification to obtain a corresponding processing result, and updating the data record associated with the praise data by using the processing result.
In one implementation, the endorsement data processing device may detect whether the module identifier in the endorsement data is a preset module identifier, so as to determine a corresponding data processing manner according to the detection result. For example, if the detection result is that the module identifier is not the preset module identifier, the first data processing may be performed on the praise data; if the detection result is that the module identifier is the preset module identifier, the first data processing can be performed on the praise data, and the second data processing can be performed on the praise data according to the preset processing mode indicated by the preset module identifier.
Wherein, preset module identification can be stored in advance on the praise data processing device, and can be set based on the requirement. The first processing mode may refer to general data processing, such as recording of praise data, statistics of praise data, and the like, which may be performed for each praise function sub-module. For example, for a record of praise data, such as in the case of a material submodule, a record of praise data for the material may be generated, which may include target user information, praise time, user account of the material publishing user, material content, praise operation type (praise type or cancel praise type), etc.; for example, as for the comment submodule, a record of the praise data for the comment can be generated, and the record can comprise target user information, praise time, a user account number of a comment posting user, comment content and the like; for example, in the case of a list sub-module, a record of the praise data for the list may be generated, and the record may include the target user information, the praise time, the list content, and so on. As another example, statistics for praise data may be statistics for praise number of a praise material, statistics for praise number of a praise comment, statistics for praise number of a praise list, and so on.
The second data processing may be a special data processing different from the above-mentioned general data processing, and the feature data processing may be preset, where the special data processing may be set according to different personalized requirements of each praise function sub-module. For example, for a certain function sub-module, a corresponding preset task may be set, and the second data processing may be triggering the preset task. For the comment submodule, the preset task may be to push other posts related to the post content corresponding to the comment to the client used by the current time of the target user.
In one implementation, the processing results may include processing results corresponding to the first data processing, and as known from the description of the first data processing, the processing results may include recording of the praise data, statistics of the praise data (such as statistics of the praise number), and so on. Then, according to these processing results, the data record associated with the original point-like data may be updated, for example, the original point-like number may be updated with the currently counted point-like number, and for example, the record of the current point-like data may be added to the original data record.
In one implementation, the data records associated with the praise data may be stored in a database, and may also be stored in a cache, where the stored data in the database may be used primarily for data grounding and providing a data source to the data analyst; the stored data in the cache may be used primarily to provide a source of data to the external service. In general, for a system, the praise data of the system is huge, so that in the actual use process, the direct data interaction with the database and the data interaction with the cache can be avoided as much as possible. The data stored in the cache can be data required by each scene included in the system, the required data can be processed in advance, the corresponding processing result is stored in the cache, and the corresponding data only needs to be acquired from the cache according to the scene when the system is used later, so that the response speed and performance of the system can be greatly improved. Wherein the required data can be processed by the praise data processing device. For example, the external services may include query services, such as queries for a comment that may provide a number of praise corresponding to the comment, queries for praise users, and so on. Then, the praise data processing device may count the number of praise users and all praise users corresponding to the comment, associate the number of praise users with all praise users, store the comment, the number of praise users and all praise users in the cache, and obtain the comment, the number of praise users or the praise users corresponding to the comment from the cache directly when the subsequent users need to query the comment.
Then, when the data record associated with the praise data is updated, the corresponding data record in the database and the cache can be updated to maintain the consistency of the data. In particular implementations, a data record for the praise data may be first determined from a database, and the data record may be referred to as a first data record, so as to update the first data record based on the processing result. A data record for the complimentary data may also be determined from the cache, which may be referred to as a second data record, to update the second data record based on the processing result.
S403: and receiving a data acquisition request which is sent by the praise data analysis equipment and is used for requesting to acquire target data, acquiring the target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment.
S404: and receiving the analysis result sent by the praise analysis equipment, and recommending the information to the client based on the analysis result.
The specific implementation of step S404 may be referred to the specific description of step S210 in the above embodiment, and will not be repeated here.
Through the implementation method, the data sources of the praise modules in the client can be divided to realize different data processing of different data sources, and corresponding module identifiers are carried when the praise data is uploaded by all the sub-modules, so that the data processing equipment can process the data of all the modules according to the module identifiers. Through the division of the modules, the data of the modules are unified to the data processing equipment for processing, so that the maintenance cost of each module can be reduced, and the praise function of each praise function module and the data in each module can be understood and coupled. The unified praise data collection processing can also enable the data corresponding to each praise function module included in the client to be communicated, complete data recording and landing are achieved, and data summarization and subsequent data analysis are facilitated.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a praise data processing apparatus according to an embodiment of the present application. The praise data processing apparatus described in the present embodiment, the praise data processing apparatus being configured to a praise data processing device, comprising:
a receiving unit 501, configured to receive praise data uploaded by a client, where the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules that the client has;
the processing unit 502 is configured to perform data processing on the praise data according to the module identifier, obtain a corresponding processing result, and update a data record associated with the praise data by using the processing result;
the receiving unit 501 is further configured to receive a data acquisition request sent by the praise data analysis device and used for requesting to acquire target data;
an obtaining unit 503, configured to obtain target data corresponding to the data obtaining request from the updated data record;
a transmitting unit 504 configured to transmit the target data to the praise data analysis device;
the receiving unit 501 is further configured to receive an analysis result sent by the praise analysis device;
And the recommending unit 505 is used for recommending information to the client based on the analysis result.
In one implementation, the processing unit 502 is specifically configured to:
whether the module identifier in the detection point approval data is a preset module identifier or not;
if the detection result is that the module identifier is not the preset module identifier, performing first data processing on the praise data;
and if the detection result is that the module identifier is the preset module identifier, performing first data processing on the praise data, and performing second data processing on the praise data according to a preset processing mode indicated by the preset module identifier.
In one implementation, the processing unit 502 is specifically configured to:
determining a first data record for the praise data from a database, and updating the first data record based on the processing result;
and determining a second data record aiming at the praise data from the cache, and updating the second data record based on the processing result.
In one implementation, the recommendation unit 505 is specifically configured to:
and recommending the materials to the appointed user based on the material recommendation type.
In one implementation, the recommendation unit 505 is specifically configured to:
And determining comment recommendation sequences of the posting users according to the posting user sequencing results, and displaying comments on a comment interface of the client according to the comment recommendation sequences.
In one implementation, the recommendation unit 505 is specifically configured to:
and determining a list recommendation sequence of the point-to-be-printed list according to the list type ranking result, and displaying the list on a list interface of the client according to the list recommendation sequence.
It can be appreciated that each functional module of the praise data processing apparatus of the present embodiment may be implemented according to the method in fig. 2 or fig. 4 of the above-mentioned method embodiment, and the specific implementation process thereof may refer to the related description in fig. 2 or fig. 4 of the above-mentioned method embodiment, which is not repeated herein.
In this embodiment, the receiving unit 501 receives the praise data uploaded by the client, where the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in multiple praise function sub-modules of the client; the processing unit 502 performs data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updates a data record associated with the praise data by using the processing result; the receiving unit 501 receives a data acquisition request for requesting acquisition of target data sent by the praise data analysis device; the obtaining unit 503 obtains the target data corresponding to the data obtaining request from the updated data record; the transmitting unit 504 transmits the target data to the praise data analyzing apparatus; the receiving unit 501 receives the analysis result sent by the praise analysis device; the recommending unit 505 recommends information to the client based on the analysis result. By implementing the method, the recommendation of the gas station can be performed based on the plurality of dimension characteristics, and the recommendation rationality is improved.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a praise data processing apparatus according to an embodiment of the present application. The praise data processing apparatus includes: processor 601, memory 602, and network interface 603. Data may be interacted between the processor 601, the memory 602, and the network interface 603.
The processor 601 may be a central processing unit (Central Processing Unit, CPU) which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 602 may include read only memory and random access memory, and provides program instructions and data to the processor 601. A portion of memory 602 may also include random access memory. Wherein the processor 601, when calling the program instructions, is configured to execute:
Receiving praise data uploaded by a client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client;
performing data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by using the processing result;
receiving a data acquisition request sent by the praise data analysis equipment and used for requesting to acquire target data, acquiring the target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment;
and receiving an analysis result sent by the praise analysis equipment, and recommending information to the client based on the analysis result.
In one implementation, the processor 601 is specifically configured to:
whether the module identifier in the detection point approval data is a preset module identifier or not;
if the detection result is that the module identifier is not the preset module identifier, performing first data processing on the praise data;
and if the detection result is that the module identifier is the preset module identifier, performing first data processing on the praise data, and performing second data processing on the praise data according to a preset processing mode indicated by the preset module identifier.
In one implementation, the processor 601 is specifically configured to:
determining a first data record for the praise data from a database, and updating the first data record based on the processing result;
and determining a second data record aiming at the praise data from the cache, and updating the second data record based on the processing result.
In one implementation, the processor 601 is specifically configured to:
and recommending the materials to the appointed user based on the material recommendation type.
In one implementation, the processor 601 is specifically configured to:
and determining comment recommendation sequences of the posting users according to the posting user sequencing results, and displaying comments on a comment interface of the client according to the comment recommendation sequences.
In one implementation, the processor 601 is specifically configured to:
and determining a list recommendation sequence of the point-to-be-printed list according to the list type ranking result, and displaying the list on a list interface of the client according to the list recommendation sequence.
In a specific implementation, the processor 601 and the memory 602 described in the embodiments of the present application may perform the implementation described in the praise data processing method provided in fig. 2 or fig. 4 of the embodiments of the present application, and may also perform the implementation of the praise data processing apparatus described in fig. 5 of the embodiments of the present application, which is not described herein again.
In this embodiment, the processor 601 receives the praise data uploaded by the client, where the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client; performing data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by using the processing result; receiving a data acquisition request sent by the praise data analysis equipment and used for requesting to acquire target data, acquiring the target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment; and receiving an analysis result sent by the praise analysis equipment, and recommending information to the client based on the analysis result. By implementing the method, the recommendation of the gas station can be performed based on the plurality of dimension characteristics, and the recommendation rationality is improved.
The embodiment of the application also provides a computer readable storage medium, wherein program instructions are stored in the computer readable storage medium, and the program can include part or all of the steps of the praise data processing method in the corresponding embodiment of fig. 2 or fig. 4 when being executed.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the described order of action, as some steps may take other order or be performed simultaneously according to the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic or optical disk, and the like.
It is emphasized that to further guarantee the privacy and security of the data, the data may also be stored in a blockchain node. The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The foregoing has described in detail the systems, methods, apparatuses and storage medium for processing like data provided by the embodiments of the present application, and specific examples have been applied herein to illustrate the principles and embodiments of the present application, and the description of the foregoing embodiments is only for aiding in the understanding of the methods and core ideas of the present application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims (9)

1. The system is characterized by comprising a client with a praise function, a praise data processing device and a praise data analysis device, wherein the client is provided with a plurality of praise function sub-modules, each praise function sub-module sends corresponding praise data to the praise data processing device through a data forwarding interface, and the data forwarding interface is integrated through a module interface corresponding to each praise function sub-module; wherein:
the praise data processing device is used for receiving praise data uploaded by the client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in the plurality of praise function sub-modules; performing data processing on the praise data according to the module identifier to obtain a corresponding processing result; updating the data record associated with the praise data by using the processing result; the praise data processing device is specifically configured to, when performing data processing on the praise data according to the module identifier: whether the module identifier in the detection point approval data is a preset module identifier or not; if the detection result is that the module identifier is not the preset module identifier, performing first data processing on the praise data; the first processing mode refers to general data processing, wherein the general data processing comprises recording of praise data and statistics of the praise data; if the detection result is that the module identifier is a preset module identifier, performing first data processing on the praise data, and performing second data processing on the praise data according to a preset processing mode indicated by the preset module identifier; the second data processing refers to special data processing, and the special data processing is set according to different personalized requirements of each praise function sub-module;
The praise data processing device is further used for receiving a data acquisition request sent by the praise data analysis device and used for requesting to acquire target data; acquiring target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment;
the praise data analysis equipment is used for receiving the target data and carrying out data analysis according to the target data to obtain a corresponding analysis result; transmitting the analysis result to the praise data processing device;
the praise data processing device is also used for receiving the analysis result sent by the praise analysis device and recommending information to the client based on the analysis result.
2. The praise data processing system of claim 1, wherein said client is further configured to:
when the praise operation of the target user is detected, obtaining praise data aiming at the praise operation, and determining a target point praise function sub-module where the praise operation is located based on the praise data;
acquiring a module identifier of the target point approval function sub-module, and carrying the module identifier in the approval data;
And sending the praise data carrying the module identifier to the praise data processing equipment.
3. The praise data processing system according to claim 1, wherein said praise data processing apparatus, when updating the data record associated with said praise data with said processing result, is specifically configured to:
determining a first data record for the praise data from a database, and updating the first data record based on the processing result;
and determining a second data record aiming at the praise data from the cache, and updating the second data record based on the processing result.
4. The praise data processing system of claim 1, wherein said plurality of praise function sub-modules comprises a materials sub-module, said target data comprising all praise materials for a given user over a predetermined period of time;
the praise data analysis device is specifically configured to, when performing data analysis according to the target data to obtain a corresponding processing result: determining the material type corresponding to each praise material, and counting the number of praise materials of each praise material under each material type; determining the material recommendation type of the appointed user according to the number of the praise materials;
The praise data processing device is specifically configured to, when information recommendation is performed on the client based on the analysis result: and recommending the materials to the appointed user based on the material recommendation type.
5. The praise data processing system according to claim 1, wherein said plurality of praise function sub-modules comprises a comment sub-module, said target data comprising all praise comments and corresponding posting users for each praise comment within a preset time period;
the praise data analysis device is specifically configured to, when performing data analysis according to the target data to obtain a corresponding processing result: determining a plurality of posting users included in all the praise comments, and counting the number of praise comments of each posting user; sorting the plurality of publishing users based on the order of the number of praise comments from large to small to obtain a ranking result of the publishing users;
the praise data processing device is specifically configured to, when information recommendation is performed on the client based on the analysis result: and determining comment recommendation sequences of the posting users according to the posting user sequencing results, and displaying comments on a comment interface of the client according to the comment recommendation sequences.
6. The praise data processing system according to claim 1, wherein the plurality of praise function sub-modules comprises a list sub-module, the target data comprising a plurality of praise lists and a list praise number corresponding to each praise list;
the praise data analysis device is specifically configured to, when performing data analysis according to the target data to obtain a corresponding processing result: determining a list type of each praise list; counting the number of list type praise under each list type based on the number of list praise corresponding to each praise list; ranking the list types based on the order of the number of the list type praise points from big to small to obtain a list type ranking result; transmitting the list type ranking result to the praise data processing equipment;
the praise data processing device is specifically configured to, when information recommendation is performed on the client based on the analysis result: and determining a list recommendation sequence of the point-to-be-printed list according to the list type ranking result, and displaying the list on a list interface of the client according to the list recommendation sequence.
7. A praise data processing method, the praise data processing method being applied to a praise data processing apparatus in a praise data processing system, the method comprising:
Receiving praise data uploaded by a client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client;
performing data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by using the processing result; the data processing of the praise data according to the module identifier comprises the following steps: whether the module identifier in the detection point approval data is a preset module identifier or not; if the detection result is that the module identifier is not the preset module identifier, performing first data processing on the praise data; the first processing mode refers to general data processing, wherein the general data processing comprises recording of praise data and statistics of the praise data; if the detection result is that the module identifier is a preset module identifier, performing first data processing on the praise data, and performing second data processing on the praise data according to a preset processing mode indicated by the preset module identifier; the second data processing refers to special data processing, and the special data processing is set according to different personalized requirements of each praise function sub-module;
Receiving a data acquisition request sent by the praise data analysis equipment and used for requesting to acquire target data, acquiring the target data corresponding to the data acquisition request from the updated data record, and sending the target data to the praise data analysis equipment;
and receiving an analysis result sent by the praise analysis equipment, and recommending information to the client based on the analysis result.
8. A praise data processing apparatus, said apparatus comprising:
the receiving unit is used for receiving the praise data uploaded by the client, wherein the praise data carries a module identifier of a target point praise function sub-module, and the target point praise function sub-module is any praise function sub-module in a plurality of praise function sub-modules of the client;
the processing unit is used for carrying out data processing on the praise data according to the module identifier to obtain a corresponding processing result, and updating a data record associated with the praise data by utilizing the processing result; the data processing of the praise data according to the module identifier comprises the following steps: whether the module identifier in the detection point approval data is a preset module identifier or not; if the detection result is that the module identifier is not the preset module identifier, performing first data processing on the praise data; the first processing mode refers to general data processing, wherein the general data processing comprises recording of praise data and statistics of the praise data; if the detection result is that the module identifier is a preset module identifier, performing first data processing on the praise data, and performing second data processing on the praise data according to a preset processing mode indicated by the preset module identifier; the second data processing refers to special data processing, and the special data processing is set according to different personalized requirements of each praise function sub-module;
The receiving unit is further used for receiving a data acquisition request which is sent by the praise data analysis equipment and used for requesting to acquire target data;
the acquisition unit is used for acquiring target data corresponding to the data acquisition request from the updated data record;
a transmitting unit configured to transmit the target data to the praise data analyzing apparatus;
the receiving unit is further used for receiving the analysis result sent by the praise analysis device;
and the recommending unit is used for recommending information to the client based on the analysis result.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of claim 7.
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