CN107341238B - Data processing method and system - Google Patents
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Abstract
The present disclosure provides a data processing method, including: receiving a data query request; inquiring a first data model based on the data inquiry request to obtain the user quantity of the target object under the specified operation behavior, wherein the first data model is at least used for storing the corresponding user quantity of the plurality of objects under various operation behaviors; inquiring a second data model associated with the first data model based on the user quantity obtained by inquiry so as to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the user information is used for describing users having the specified operation behavior on the target object; and outputting the user information. The present disclosure also provides a data processing system and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a data processing method and system.
Background
In the big data era, a user can generate a large amount of behavior data every day, the action of the big data influences the aspects of people, and how to process the data is significant. In the prior art, in the face of a large amount of data, the data is generally classified according to the service types according to the service requirements, and then a large amount of data models are established. Based on these data models, one can predict the direction of behavior of the user.
However, in the course of implementing the disclosed concept, the inventors found that there are at least the following problems in the prior art: the scheme provided by the prior art has the disadvantages of more models established, high use complexity and serious resource waste.
Disclosure of Invention
In view of this, the present disclosure provides a data processing method and system for saving system resources.
One aspect of the present disclosure provides a data processing method, including: receiving a data query request; inquiring a first data model based on the data inquiry request to obtain the user amount of the target object under the specified operation behavior, wherein the first data model is at least used for storing the user amount of the plurality of objects under the plurality of operation behaviors; querying a second data model associated with the first data model based on the queried user quantity to find user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the user information is used for describing a user having the specified operation behavior on the target object; and outputting the user information.
According to an embodiment of the present disclosure, the method further includes an operation of constructing the second data model, where the operation includes: acquiring identification information of a target user; acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object; judging whether the different types of operation data meet preset conditions or not; and if so, constructing the second data model based on the identification information of the target user and the different types of operation data.
According to an embodiment of the present disclosure, the method further includes: and if not, filtering the different types of operation data generated by the target user performing different operation behaviors on the corresponding object.
According to an embodiment of the present disclosure, the obtaining different types of operation data generated by the target user performing different operation behaviors on the corresponding object includes: and acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period.
According to an embodiment of the present disclosure, querying, based on the queried user quantity, a second data model associated with the first data model to find user information corresponding to the user quantity of the target object under the specified operation behavior includes: acquiring identification information of the target object, wherein the identification information is stored in the first data model; determining the second data model associated with the first data model based on the identification information; and querying the second data model based on the user quantity obtained by the query and the identification information to obtain user information corresponding to the user quantity of the target object under the specified operation behavior.
Another aspect of the present disclosure provides a data processing system comprising: the receiving module is used for receiving a data query request; the first query module is used for querying a first data model based on the data query request so as to obtain the user quantity of the target object under the specified operation behavior, and the first data model is at least used for storing the corresponding user quantity of the plurality of objects under various operation behaviors; a second query module, configured to query, based on the user quantity obtained through query, a second data model associated with the first data model to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, where the user information is used to describe a user having the specified operation behavior on the target object; and the output module is used for outputting the user information.
According to an embodiment of the present disclosure, the system further includes a building module for building the operation of the second data model, the building module includes: a first obtaining unit configured to obtain identification information of a target user; the second acquisition unit is used for acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object; the judging unit is used for judging whether the different types of operation data meet preset conditions or not; and a construction unit, configured to construct the second data model based on the identification information of the target user and the different types of operation data when the different types of operation data satisfy a preset condition.
According to an embodiment of the present disclosure, the building module further includes: and a filtering unit, configured to filter, when the different types of operation data do not satisfy a preset condition, the different types of operation data generated by the target user performing different operation behaviors on the corresponding object.
According to an embodiment of the present disclosure, the second obtaining unit is further configured to obtain different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period.
According to an embodiment of the present disclosure, the second query module includes: a third obtaining unit, configured to obtain identification information of the target object, where the identification information is stored in the first data model; a determination unit configured to determine the second data model associated with the first data model based on the identification information; and the query unit is used for querying the second data model based on the user quantity obtained by the query and the identification information so as to obtain the user information corresponding to the user quantity of the target object under the specified operation behavior.
Another aspect of the present disclosure provides a non-volatile storage medium storing computer-executable instructions for implementing the data processing method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Another aspect of the present disclosure provides a computer system comprising: one or more processors; a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the data processing method of any of the above.
According to the embodiment of the present disclosure, because the user amounts corresponding to the plurality of operation behaviors for the plurality of objects are stored in the first data model, the related data of the users for the plurality of operation behaviors for the objects are stored in the second data model, and the first data model and the second data model are associated, the problem of resource waste caused by a large number of data models in the prior art can be at least solved, and therefore, the technical effect of saving system resources can be achieved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture to which the disclosed method and system for data processing may be applied;
FIG. 2 schematically shows a flow diagram of a data processing method according to an embodiment of the present disclosure;
FIG. 3A schematically illustrates a flow chart for building a second data model according to an embodiment of the present disclosure;
FIG. 3B schematically shows a flow diagram of another data processing method according to an embodiment of the present disclosure;
FIG. 4 schematically shows a block diagram of a data model according to an embodiment of the disclosure;
FIG. 5 schematically shows a block diagram of a data processing system according to an embodiment of the present disclosure;
FIG. 6 schematically shows a block diagram of a build module according to an embodiment of the disclosure;
FIG. 7 schematically illustrates a block diagram of a second query module, in accordance with an embodiment of the present disclosure; and
FIG. 8 schematically shows a block diagram of a computer system suitable for implementing a data processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The words "a", "an" and "the" and the like as used herein are also intended to include the meanings of "a plurality" and "the" unless the context clearly dictates otherwise. Furthermore, the terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing system, such that the instructions, which execute via the processor, create a system that implements the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Accordingly, the techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a data processing method and a data processing system, wherein the method comprises the following steps: receiving a data query request; inquiring a first data model based on the data inquiry request to obtain the user quantity of the target object under the specified operation behavior, wherein the first data model is at least used for storing the corresponding user quantity of the plurality of objects under various operation behaviors; inquiring a second data model associated with the first data model based on the user quantity obtained by inquiry so as to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the user information is used for describing users having the specified operation behavior on the target object; and outputting the user information.
Fig. 1 schematically illustrates an exemplary system architecture to which the disclosed method and system for data processing may be applied.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for shopping-like websites browsed by users using the terminal devices 101, 102, 103. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the data processing method provided by the embodiment of the present disclosure may be executed by the server 105, or may be executed by another server or a server cluster different from the server 105. Accordingly, the data processing system may be located in server 105, or may be located in another server or a cluster of servers other than server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S201 to S204, in which:
in operation S201, a data query request is received.
According to the embodiment of the disclosure, a user sends a data query request to a data model through a query statement and/or a corresponding judgment condition, and the data model needs to receive the data query request of the user so as to query the relevant information of a certain object.
In operation S202, a first data model is queried based on the data query request to find a user amount of the target object under a specified operation behavior, where the first data model is at least used for storing user amounts of the plurality of objects under various operation behaviors.
According to the embodiment of the disclosure, at least the user amount corresponding to the plurality of objects under the plurality of operation behaviors is stored in the first data model. For example, the plurality of objects include an item a, an item B, and an item C, and the plurality of operation behaviors include user's operations of paying attention to the item, browsing, and consulting. Then, the first data model has stored therein at least: the user amount of the concern item a is 100 times, the user amount of the concern item B is 200 times, and the user amount of the concern item C is 300 times; the user quantity for browsing the article A is 100 times, the user quantity for browsing the article B is 200 times, and the user quantity for browsing the article C is 300 times; the user amount of the consultation good A is 100 times, the user amount of the consultation good B is 200 times, and the user amount of the consultation good C is 300 times. The query of the first data model based on the data query request may be a request to query the user volume of the item a under a plurality of specified operational behaviors, such as consultation, attention and browsing operations.
In operation S203, based on the user quantity obtained through the query, a second data model associated with the first data model is queried to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, where the user information is used to describe a user having the specified operation behavior on the target object.
According to the embodiment of the disclosure, the second data model associated with the first data model is queried according to the user quantity obtained by querying. For example, when the operation behavior of the item a is specified as the user number of 100 people in consultation, the second data model associated with the first data model is inquired, and then the user information of the consultation item a is inquired, wherein the user information may include account information or identification information for identifying the user.
In operation S204, user information is output.
According to the embodiment of the disclosure, after the user information corresponding to the user amount of the target object under the specified operation behavior is obtained through inquiry, the user information is output.
According to the embodiment of the disclosure, because the user amount corresponding to the plurality of operation behaviors for the plurality of objects is stored in the first data model, the related data of the users for the plurality of operation behaviors for the objects is stored in the second data model, and the first data model is associated with the second data model, the problem of resource waste caused by more data models in the prior art can be at least solved, and therefore, the technical effects of saving resources and quickly querying data can be realized.
The method illustrated in fig. 2 is further described with reference to fig. 3A and 3B in conjunction with specific embodiments.
FIG. 3A schematically illustrates a flow chart for building a second data model according to an embodiment of the disclosure.
As shown in FIG. 3A, according to an embodiment of the present disclosure, constructing the second data model includes operations S301-S305, wherein:
in operation S301, identification information of a target user is acquired.
In operation S302, different types of operation data generated by the target user performing different operation behaviors on the corresponding object are obtained.
In operation S303, it is determined whether different types of operation data satisfy a preset condition.
In operation S304, if yes, a second data model is constructed based on the identification information of the target user and different types of operation data.
In operation S305, if not, different types of operation data generated by the target user performing different operation behaviors on the corresponding object are filtered.
According to the embodiment of the disclosure, when the second data model is constructed, the identification information of the user needs to be associated with the operation data generated by the user performing different operation behaviors on different objects, so that the identification information of the target user needs to be acquired, and different types of operation data generated by the target user performing different operation behaviors on corresponding objects need to be acquired.
According to the embodiment of the disclosure, for example, identification information of the user a, such as account information, is acquired, and operation data generated by paying attention to and browsing the item a and consulting the item a is acquired. After obtaining the identification information and the operation data of the user a, it is necessary to determine whether the different types of operation data satisfy a preset condition, for example, the preset condition may be whether the user a has completed a transaction for the article a. If the operational data of user A does not include the completed transaction for item A, the identification information and operational data of user A are added to the second data model, i.e., the second data model is constructed based on the identification information and different types of operational data of the target user. If the operational data of user A includes the completed transaction for item A, then the identification information of user A and all operational data are filtered and no relevant information of user A exists in the second data model.
In accordance with an embodiment of the present disclosure, fig. 4 schematically illustrates a block diagram of a data model according to an embodiment of the present disclosure, and a user behavior schedule in the second data model may be as follows:
user behavior statement
| Model field | Source of field data |
| User account | Table 1 or table 2 or table 3 or table 4 |
| sku numbering | Table 1 or table 2 or table 3 or table 4 |
| Time of action | Table 1 or table 2 or table 3 or table 4 |
| Attention mark | TABLE 1 |
| Shopping mark | TABLE 2 |
| Browsing mark | TABLE 3 |
| Consultation sign | TABLE 4 |
According to the embodiment of the disclosure, an effective method for building a user behavior model is provided, and the method efficiently integrates various business requirements of users, namely different types of operation data generated by different operation behaviors are integrated. Through the model obtained after integration, the number and the use complexity of the model are reduced, the later maintenance cost and risk of the same caliber index are reduced, and the access pressure to the order form and the server cluster pressure are reduced.
According to the embodiment of the present disclosure, acquiring different types of operation data generated by a target user performing different operation behaviors on a corresponding object includes: acquiring different types of operation data generated by different operation behaviors of the target user on the corresponding object within a preset time period.
According to the embodiment of the disclosure, the preset time periods may be 30 days, 60 days and 90 days, different types of operation data generated by different operations performed on the corresponding objects by the user in different preset time periods are acquired, the operation data of the user may be counted in a multi-time dimension, and the preset time period of the operation data of the user may be expanded or reduced according to business needs.
Fig. 3B schematically shows a flowchart of another data processing method according to an embodiment of the present disclosure, and as shown in fig. 3B, querying a second data model associated with a first data model based on a user amount obtained by querying to obtain user information corresponding to the user amount of a target object under a specified operation behavior according to an embodiment of the present disclosure includes: comprising operations S401 to S403, wherein:
in operation S401, identification information of a target object is acquired, wherein the identification information is stored in a first data model.
In operation S402, a second data model associated with the first data model is determined based on the identification information.
In operation S403, based on the queried user quantity and identification information, the second data model is queried to find user information corresponding to the user quantity of the target object under the specified operation behavior.
According to the embodiment of the disclosure, the first data model and the second data model are associated through the identification information of the target object, and the second data model is queried according to the user quantity and the identification information obtained through query.
FIG. 4 schematically shows a block diagram of a data model according to an embodiment of the disclosure. The method shown in FIG. 3B is further described with reference to FIG. 4 in conjunction with specific embodiments.
As shown in fig. 4, taking a preset time period of 90 days as an example, the acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object within the preset time period includes:
table 1: and extracting the user attention detail data for 90 days from the user attention (or collection) table, and acquiring the user login account, the goods number and the attention time (behavior time). If the user pays attention to the same item for multiple times, the record with the latest attention time is taken.
Table 2: and extracting the detailed data of the user and the shopping cart in 90 days from the table of the user and the shopping cart, and acquiring the login account number, the goods number and the shopping cart adding time (action time) of the user. If the user adds the same goods for multiple times, the record with the latest adding time is taken.
Table 3: and extracting the user browsing detail data of 90 days from the user browsing table, and acquiring the user login account, the goods number and the attention time (behavior time). And if the user browses the same goods for multiple times, taking the latest record of browsing time.
Table 4: and extracting the user consultation detail data for 90 days from the user consultation table, and acquiring the login account number, the goods number and the attention time (behavior time) of the user. And if the user consults the same goods for multiple times, taking the latest record of the consultation time.
Table 5: and extracting order detail data of 90 days from the order data, wherein the judgment condition that the order is valid needs to be met.
Table 6: the latest item additional data is acquired.
Table 7: and acquiring latest goods attribution data.
Matching the table data of tables 1-4 with the order data of table 5, wherein the association condition is whether the order information exists within 90 days or not under the same login account number and the same goods number, and if the user purchases the order and the order is valid, removing the user behavior record; if the user does not purchase or buy but the order is invalid, the user behavior record is logged into the user behavior specification model (second data model) and the user behavior table from which the record was taken is tagged.
According to the embodiment of the disclosure, the item operated data summary table main key in the item operated summary model (first data model) is the item with the finest granularity. And carrying out user behavior statistics in each time range of 1 day, 3 days, 15 days, 30 days, 60 days, 90 days and the like through detail data of the user behavior detail model. And the goods information and the goods attribution information are correlated through the goods number, so that a summary information table with wide service requirement coverage is formed, and the field sequencing in the table can be adjusted according to the use habit. For example, the item operated data summary table in the first data model is as follows:
data summary table for goods operated
According to the embodiment of the disclosure, the goods operated summary model (the first data model) takes the finest granularity SKU (SKU is the smallest granularity of the goods, for example, the data combination of color and capacity of a certain cup is SKU, yellow 500ML is SKU1, yellow 300ML is SKU 2.) as a main key, and a SKU is a record, so that the system can help a business department to quickly locate the focus goods of attention, shopping cart, browsing and consultation, and further promote the single goods or special topics. When the business department locks the SKU or SKU group and wants to know the specific user who, the user group can be viewed and defined in the user behavior detail model through the SKU number.
According to the embodiment of the disclosure, the user behavior detail model (the second data model) has behavior time, and if dimensions such as 1 day, 3 days and the like in a summary table of the operation summary model of the goods do not meet the requirement of a service time range (such as 7 days of data), statistics can be carried out through the behavior time of the user operation data.
According to the embodiment of the disclosure, when more goods information is needed, the user behavior detail model and the goods operated summary model can be associated through the SKU number, so that more goods related information can be obtained. By the mode, the effects of quickly positioning the target object and accurately positioning the target user can be achieved.
According to an embodiment of the present disclosure, a data processing system is also provided.
FIG. 5 schematically shows a block diagram of a data processing system according to an embodiment of the present disclosure. As shown in fig. 5, the data processing system 500 includes a receiving module 510, a first querying module 520, a second querying module 530, and an outputting module 540, wherein:
the receiving module 510 is used for receiving a data query request.
The first query module 520 is configured to query a first data model based on the data query request to obtain the user amount of the target object under the specified operation behavior, where the first data model is at least used to store the user amount of the plurality of objects under the plurality of operation behaviors.
The second query module 530 is configured to query, based on the user quantity obtained through the query, the second data model associated with the first data model to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, where the user information is used to describe a user having the specified operation behavior on the target object.
The output module 540 is used for outputting the user information.
According to the embodiment of the disclosure, because the user amount corresponding to the plurality of operation behaviors for the plurality of objects is stored in the first data model, the related data of the users for the plurality of operation behaviors for the objects is stored in the second data model, and the first data model is associated with the second data model, the problem of resource waste caused by more data models in the prior art can be at least solved, and therefore, the technical effects of saving resources and quickly querying data can be realized.
Fig. 6 schematically shows a block diagram of a building module according to an embodiment of the disclosure.
As shown in fig. 6, according to an embodiment of the present disclosure, the system may further include a building module 550 for building an operation of the second data model, the building module 550 includes a first obtaining unit 5501, a second obtaining unit 5502, a judging unit 5503, and a building unit 5504, wherein:
the first acquiring unit 5501 is configured to acquire identification information of a target user.
The second acquiring unit 5502 is configured to acquire different types of operation data generated by different operation behaviors performed on the corresponding object by the target user.
The determination unit 5503 is configured to determine whether different types of operation data satisfy preset conditions.
The constructing unit 5504 is configured to construct the second data model based on the identification information of the target user and the different types of operation data, when the different types of operation data satisfy the preset condition.
According to an embodiment of the present disclosure, the building block further comprises: and the filtering unit is used for filtering different types of operation data generated by the target user performing different operation behaviors on the corresponding object under the condition that the different types of operation data do not meet the preset conditions.
According to the embodiment of the disclosure, an effective construction module of a user behavior model is provided, and the construction module integrates various business requirements of users efficiently, namely integrates different types of operation data generated by different operation behaviors. Through the model obtained after integration, the number and the use complexity of the model are reduced, the later maintenance cost and risk of the same caliber index are reduced, and the access pressure to the order form and the server cluster pressure are reduced.
According to the embodiment of the disclosure, the second obtaining unit is further configured to obtain different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period.
According to the embodiment of the disclosure, the second obtaining unit obtains different types of operation data generated by different operation behaviors of the user on the corresponding object in different preset time periods, the operation data of the user can be counted in a multi-time dimension, and the preset time period of the operation data of the user can be expanded or reduced according to business needs.
FIG. 7 schematically shows a block diagram of a second query module according to an embodiment of the disclosure. As shown in fig. 7, according to an embodiment of the present disclosure, the second query module 530 includes a third obtaining unit 5301, a determining unit 5302, and a querying unit 5303, in which:
the third obtaining unit 5301 is configured to obtain identification information of the target object, where the identification information is stored in the first data model.
The determining unit 5302 is configured to determine a second data model associated with the first data model based on the identification information.
The querying unit 5303 is configured to query the second data model based on the queried user quantity and the identification information to obtain user information corresponding to the user quantity of the target object under the specified operation behavior.
According to the embodiment of the disclosure, the second query module can achieve the effects of quickly positioning the target object and accurately positioning the target user.
It should be noted that, the data processing system part in the embodiment of the present disclosure corresponds to the data processing method part in the embodiment of the present disclosure, and the description of the data processing system part specifically refers to the data processing method part, which is not described herein again.
FIG. 8 schematically shows a block diagram of a computer system suitable for implementing a data processing method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 8 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 8, a computer system 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 610 may also include onboard memory for caching purposes. Processor 610 may include a single processing unit or multiple processing units for performing different actions of the method flows described with reference to fig. 2-3B in accordance with embodiments of the present disclosure.
In the RAM 603, various programs and data necessary for the operation of the system 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations described above with reference to fig. 2 to 3B by executing programs in the ROM 602 and/or the RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations described above with reference to fig. 2-3B by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, system 600 may also include an input/output (I/O) interface 605, input/output (I/O) interface 605 also connected to bus 604. The system 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
According to an embodiment of the present disclosure, the method described above with reference to the flow chart may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer 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 of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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. In the present disclosure, a computer 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. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-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 computer readable signal medium may also be any computer readable medium that is not a computer 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 computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. According to embodiments of the present disclosure, a computer-readable medium may include the ROM 602 and/or RAM 603 described above and/or one or more memories other than the ROM 602 and RAM 603.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present disclosure also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to perform: receiving a data query request; inquiring a first data model based on the data inquiry request to obtain the user amount of the target object under the specified operation behavior, wherein the first data model is at least used for storing the user amount of the plurality of objects under the plurality of operation behaviors; querying a second data model associated with the first data model based on the queried user quantity to find user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the user information is used for describing a user having the specified operation behavior on the target object; and outputting the user information. The method further includes an operation of constructing the second data model, the operation including: acquiring identification information of a target user; acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object; judging whether the different types of operation data meet preset conditions or not; and if so, constructing the second data model based on the identification information of the target user and the different types of operation data. The method further comprises the following steps: and if not, filtering the different types of operation data generated by the target user performing different operation behaviors on the corresponding object. The obtaining of different types of operation data generated by the target user performing different operation behaviors on the corresponding object includes: and acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period. Querying a second data model associated with the first data model based on the queried user quantity to find user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the querying comprises: acquiring identification information of the target object, wherein the identification information is stored in the first data model; determining the second data model associated with the first data model based on the identification information; and querying the second data model based on the user quantity obtained by the query and the identification information to obtain user information corresponding to the user quantity of the target object under the specified operation behavior.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (10)
1. A method of data processing, comprising:
receiving a data query request;
inquiring a first data model based on the data inquiry request to obtain the user amount of the target object under the specified operation behavior, wherein the first data model is at least used for storing the user amount of the plurality of objects under the plurality of operation behaviors;
querying a second data model associated with the first data model based on the queried user quantity to find user information corresponding to the user quantity of the target object under the specified operation behavior, wherein the user information is used for describing users having the specified operation behavior on the target object;
outputting the user information;
wherein the method further comprises an operation of constructing the second data model, the operation comprising:
acquiring identification information of a target user;
acquiring different types of operation data generated by the target user performing different operation behaviors on corresponding objects from user behavior tables of multiple service types, wherein the user behavior tables comprise a user attention or collection table, a user shopping cart table, a user browsing table and/or a user consultation table;
judging whether the different types of operation data meet preset conditions or not; and
if so, constructing the second data model based on the identification information of the target user and the different types of operation data;
the preset condition is that the operation data of the target user on the corresponding object does not include data of transaction completion;
the method further comprises the following steps: and if the operation data of the target user on the corresponding object comprises data of transaction completion, filtering out the operation data of the target user on the corresponding object.
2. The method of claim 1, wherein the method further comprises:
and if not, filtering the different types of operation data generated by the target user performing different operation behaviors on the corresponding object.
3. The method of claim 1, wherein obtaining different types of operational data resulting from the target user performing different operational behaviors on corresponding objects comprises:
and acquiring different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period.
4. The method of claim 1, wherein querying a second data model associated with the first data model based on a queried amount of users to find user information corresponding to the amount of users of the target object under the specified operational behavior comprises:
acquiring identification information of the target object, wherein the identification information is stored in the first data model;
determining the second data model associated with the first data model based on the identification information; and
and querying the second data model based on the user quantity obtained by the query and the identification information so as to obtain user information corresponding to the user quantity of the target object under the specified operation behavior.
5. A data processing system comprising:
the receiving module is used for receiving a data query request;
the first query module is used for querying a first data model based on the data query request so as to obtain the user amount of the target object under the specified operation behavior, and the first data model is at least used for storing the corresponding user amount of the plurality of objects under various operation behaviors;
a second query module, configured to query, based on the user quantity obtained through query, a second data model associated with the first data model to obtain user information corresponding to the user quantity of the target object under the specified operation behavior, where the user information is used to describe a user having the specified operation behavior on the target object;
the output module is used for outputting the user information;
the system further includes a building module for operations to build the second data model, the building module including:
a first obtaining unit configured to obtain identification information of a target user;
a second obtaining unit, configured to obtain, from user behavior tables of multiple service types, different types of operation data generated when the target user performs different operation behaviors on a corresponding object, where the user behavior table includes a user attention or collection table, a user shopping cart table, a user browsing table, and/or a user consultation table;
the judging unit is used for judging whether the different types of operation data meet preset conditions or not; and
the construction unit is used for constructing the second data model based on the identification information of the target user and the different types of operation data under the condition that the different types of operation data meet preset conditions;
the preset condition is that the operation data of the target user on the corresponding object does not include data of transaction completion;
the construction module is further used for filtering out the operation data implemented by the target user on the corresponding object if the operation data implemented by the target user on the corresponding object comprises data of transaction completion.
6. The system of claim 5, wherein the build module further comprises:
and the filtering unit is used for filtering the different types of operation data generated by the target user performing different operation behaviors on the corresponding object under the condition that the different types of operation data do not meet preset conditions.
7. The system of claim 5, wherein the second obtaining unit is further configured to obtain different types of operation data generated by the target user performing different operation behaviors on the corresponding object within a preset time period.
8. The system of claim 5, wherein the second query module comprises:
a third obtaining unit, configured to obtain identification information of the target object, where the identification information is stored in the first data model;
a determining unit configured to determine the second data model associated with the first data model based on the identification information; and
and the query unit is used for querying the second data model based on the user quantity obtained by querying and the identification information so as to obtain the user information corresponding to the user quantity of the target object under the specified operation behavior.
9. A computer readable storage medium having stored thereon executable instructions for implementing the data processing method of any one of claims 1 to 4 when executed by a processor.
10. A computer system, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the data processing method of any of claims 1 to 4.
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| CN110287225B (en) * | 2019-06-28 | 2021-04-02 | 宇宙世代信息技术（深圳）有限公司 | Data coding management method and device, computer equipment and storage medium |
| CN113496321A (en) * | 2020-03-19 | 2021-10-12 | 北京京东振世信息技术有限公司 | Method and device for determining workload |
| CN113220657B (en) * | 2021-05-14 | 2022-12-06 | 上海哔哩哔哩科技有限公司 | Data processing method and device and computer equipment |
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