CN117093750A

CN117093750A - Data processing method, device, equipment and medium

Info

Publication number: CN117093750A
Application number: CN202210516037.4A
Authority: CN
Inventors: 陈春勇
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2022-05-12
Filing date: 2022-05-12
Publication date: 2023-11-21

Abstract

The embodiment of the application provides a data processing method, a device, equipment and a medium. Wherein the method comprises the following steps: in response to a scanning operation for a unique identification code carried by target data, displaying an information presentation page associated with the target data: the target data are displayed in a terminal screen of the public equipment; acquiring object operation data and object attribute information associated with an information display page, and acquiring release object data and release scene data associated with a unique identification code; and encapsulating the object operation data, the object attribute information, the object data and the scene data into data to be uplinked with an association relationship, and storing the data to be uplinked, wherein the data to be uplinked is used for determining the object to be launched of the target data in the subsequent scene. By adopting the embodiment of the application, the management effectiveness of the released data can be improved, and the data release cost can be reduced.

Description

Data processing method, device, equipment and medium

Technical Field

The present application relates to the field of internet technologies, and in particular, to a data processing method, apparatus, device, and medium.

Background

With the rapid development of internet technology, internet data is widely put into mobile terminals (e.g., mobile phones, tablets, etc.) and non-mobile terminals (e.g., televisions, elevator cab display devices, outdoor electronic display devices, etc.), so that users can obtain the put data through different channels. When the internet data is put into the mobile terminal, the mobile terminal user can browse, collect, forward and the like the network data, and the related operations of the mobile terminal user can be detected in real time; when the internet data is put into the non-mobile terminal, the relevant operation data of the non-mobile terminal user aiming at the internet data cannot be obtained, that is, the internet data put into the non-mobile terminal cannot obtain effective feedback information, which is equivalent to the fact that when the input internet data is analyzed later, the relevant data of the non-mobile terminal user is lacking, which affects the management of the input internet data and causes the input cost of the internet data to be too high.

Disclosure of Invention

The embodiment of the application provides a data processing method, a device, equipment and a medium, which can improve the management effectiveness of released data and reduce the data release cost.

In one aspect, an embodiment of the present application provides a data processing method, including:

in response to a scanning operation for a unique identification code carried by target data, displaying an information presentation page associated with the target data: the target data are displayed in a terminal screen of the public equipment;

acquiring object operation data and object attribute information associated with an information display page, and acquiring release object data and release scene data associated with a unique identification code;

encapsulating object operation data, object attribute information, object throwing data and scene throwing data into data to be uplinked with association relation, and performing uplinking storage on the data to be uplinked; the data to be uplinked is used for determining the launched object of the target data in the subsequent launching scene.

In one aspect, an embodiment of the present application provides a data processing apparatus, including:

the scanning module is used for responding to the scanning operation of the unique identification code carried by the target data and displaying an information display page associated with the target data: the target data are displayed in a terminal screen of the public equipment;

the data acquisition module is used for acquiring object operation data and object attribute information associated with the information display page, and acquiring delivery object data and delivery scene data associated with the unique identification code;

The storage module is used for packaging the object operation data, the object attribute information, the object throwing data and the scene throwing data into data to be uplinked with an association relationship, and carrying out uplinking storage on the data to be uplinked; the data to be uplinked is used for determining the launched object of the target data in the subsequent launching scene.

In one or more embodiments, the memory module includes:

the association relation setting unit is used for setting association relation among the object operation data, the object attribute information, the object throwing data and the scene throwing data to obtain association data;

the hash operation unit is used for carrying out hash operation on the associated data to obtain hash data corresponding to the associated data, and packaging the hash data into data to be uplink;

the broadcasting unit is used for writing the data to be uplinked into the data block, broadcasting the data block in the block chain network so as to enable the consensus node in the block chain network to perform consensus operation on the data block;

and the accounting unit is used for adding the data block to the blockchain if the data block consensus passes.

In one or more embodiments, the data processing apparatus further comprises:

the first acquisition module is used for acquiring an operation data set of target data from the block chain, determining a plurality of feedback objects corresponding to the target data according to the operation data set of the target data, and acquiring object cluster characteristics of the plurality of feedback objects; the operation data set of the target data includes object operation data;

The second acquisition module is used for acquiring the object characteristics of each object to be selected in the object set to be selected, and determining the object to be put in from the object set to be selected according to the object characteristics and the object cluster characteristics of each object to be selected;

the third acquisition module is used for acquiring an operation data set of the object to be put from the blockchain and acquiring the data set characteristics of the object to be put according to the operation data set of the object to be put;

a fourth obtaining module, configured to obtain a matching value between a data set feature of the object to be put and a data feature corresponding to the target data;

and the delivery module is used for delivering the target data to the object to be delivered if the matching value is larger than the first threshold value.

In one or more embodiments, the first acquisition module includes:

the first acquisition unit is used for acquiring operation data of the feedback object a on the first data and acquiring a preference value of the feedback object a on the first data according to the operation data of the feedback object a on the first data; the first data comprises target data, and the feedback object a belongs to a plurality of feedback objects;

the second acquisition unit is used for acquiring target operation data from the operation data of the feedback object a on the first data according to the preference value of the feedback object a on the first data, and acquiring target object attributes corresponding to the feedback time of the target operation data from the object attribute set of the feedback object a;

The third acquisition unit is used for acquiring object preference characteristics of the feedback object a according to the object throwing data, the scene throwing data and the object attribute corresponding to the target operation data;

a fourth acquisition unit configured to acquire an object weight of the feedback object a and a data weight of the first data; and acquiring object cluster characteristics of a plurality of feedback objects according to the object weights and the object preference characteristics of the feedback objects a and the data weights of the first data.

In one or more embodiments, the fourth acquisition unit includes:

the first acquisition subunit is used for acquiring a first number of operation data sets of the feedback object a and a first value of the feedback object a, and acquiring a second number of operation data sets of the first data and a second value of the first data;

the second obtaining subunit is configured to obtain the object weight of the feedback object a according to the first number and the first value, and obtain the data weight of the first data according to the second number and the second value.

In one or more embodiments, the operational data set of the object to be put includes operational data of the object to be put on the second data;

the third acquisition module includes:

the fifth acquisition unit is used for acquiring a preference value of the object to be put on the second data according to the operation data of the object to be put on the second data;

The sixth acquisition unit is used for acquiring data preference characteristics of the second data according to the throwing object data and throwing scene data of the second data and preference values of the objects to be thrown on the second data;

the seventh acquisition unit is used for acquiring the data weight of the second data and the object weight of the object to be put in; and acquiring the data set characteristics of the object to be put in according to the data weight and the data preference characteristics of the second data.

In one or more embodiments, the data processing apparatus further comprises:

the receiving module is used for receiving a uplink request of the object to be put on to the operation data of the second data;

the fifth acquisition module is used for acquiring the throwing value of the object to be thrown according to the object attribute set of the object to be thrown, the historical operation data and throwing object data corresponding to the historical operation data;

the uplink module is used for performing uplink storage on operation data of the object to be released aiming at the target data, and release object data and release scene data corresponding to the operation data if the release value of the object to be released is larger than a second threshold value.

In one or more embodiments, the data processing apparatus further comprises:

a sixth acquisition module, configured to acquire a tag set and the number of coverage objects of the target data according to attribute information of the target data; acquiring tag sets respectively corresponding to a plurality of candidate objects; if the tag set of the candidate object b in the plurality of candidate objects comprises the tag set of the target data, taking the candidate object b as a first object; if the number of the first objects is smaller than the number of the covered objects, performing object expansion based on the tag set of the first objects to obtain second objects; the sum between the number of first objects and the number of second objects is equal to the number of overlay objects;

The delivery module is also used for delivering target data to the first object and the second object.

An aspect of an embodiment of the present application provides a computer device, including a memory and a processor, where the memory is connected to the processor, and the memory is used to store a computer program, and the processor is used to call the computer program, so that the computer device performs the method provided in the foregoing aspect of the embodiment of the present application.

An aspect of an embodiment of the present application provides a computer readable storage medium, in which a computer program is stored, the computer program being adapted to be loaded and executed by a processor, to cause a computer device having a processor to perform the method provided in the above aspect of an embodiment of the present application.

According to one aspect of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in the above aspect.

The embodiment of the application can respond to the scanning operation of the unique identification code carried by the target data, and display the information display page associated with the target data, wherein the target data is displayed in the terminal screen of the public equipment; and then object operation data and object attribute information associated with the information display page, and release object data and release scene data associated with the unique identification code can be obtained, the object operation data, the object attribute information, the release object data and the release scene data are packaged into data to be uplink with an association relationship, the data to be uplink is stored, and the data to be uplink can be used for determining a released object of target data in a subsequent release scene. It can be seen that, for the target data put in the public device, the target data may carry a unique identifier, an information display page associated with the target data may be displayed by scanning the unique identifier, and object operation data generated in the information display page and object attribute information corresponding to the objects generating the object operation data may be obtained; and the unique identification code can be used for associating the put object data corresponding to the target data with the put scene data, so that the data can be associated and stored in the blockchain, namely, the related data of the put target data can be effectively obtained, the tracing of the target data is realized through the blockchain, the management effectiveness of the put data can be improved, and the data put cost is reduced.

Drawings

In order to more clearly illustrate the embodiments of the 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, it being obvious that the drawings in the following description are only some embodiments of the 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 structural diagram of a network architecture according to an embodiment of the present application;

fig. 2 is a schematic view of a scenario of data delivery provided by an embodiment of the present application;

FIG. 3 is a schematic flow chart of a data processing method according to an embodiment of the present application;

fig. 4A, fig. 4B, and fig. 4C are schematic diagrams of a scene of advertising according to an embodiment of the present application;

FIG. 5A is a schematic diagram showing the comparison of single visitor coverage in single-ended delivery and information flow delivery according to an embodiment of the present application;

fig. 5B is a schematic diagram comparing acquisition costs of a single visitor during single-end delivery and information flow delivery according to an embodiment of the present application;

FIG. 6 is a flowchart of an advertisement delivery method according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a user lifecycle value model, provided by an embodiment of the present application;

FIG. 8 is a schematic flow chart of a target data delivery processing method provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 10 is a schematic structural diagram of a computer device 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 completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a network architecture according to an embodiment of the present application. As shown in fig. 1, the network architecture may include a server 10d and a user terminal cluster, which may include one or more user terminals, without limiting the number of user terminals. As shown in fig. 1, the user terminal cluster may specifically include a user terminal 10a, a user terminal 10b, a user terminal 10c, and the like.

The server 10d may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, a content delivery network (content delivery network, CDN), basic cloud computing services such as big data and an artificial intelligence platform.

Alternatively, the server 10d may be at least one node device in a Blockchain (Blockchain) network. Blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission (P2P transmission), consensus mechanism, encryption algorithm and the like. The blockchain is essentially a decentralised database, which is a series of data blocks generated by cryptographic methods, each data block containing a batch of information of network transactions for verifying the validity (anti-counterfeiting) of the information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, and an application services layer.

The blockchain underlying platform may include processing modules for basic services, smart contracts, operation maintenance, and the like. The basic service module is deployed on all block chain node devices, is used for verifying the validity of a service request, recording the service request on a storage after the effective request is identified, and for a new service request, the basic service firstly analyzes interface adaptation and authenticates the interface adaptation, encrypts service information (identification management) through an identification algorithm, and transmits the encrypted service information to a shared account book (network communication) completely and consistently, and records and stores the service information; the intelligent contract module is responsible for registering and issuing contracts, triggering contracts and executing contracts, a developer can define contract logic through a certain programming language, issue the contract logic to a blockchain (contract registering), invoke keys or other event triggering execution according to the logic of contract clauses to complete the contract logic, and simultaneously provide a function of registering contract upgrading; the operation maintenance module is mainly responsible for deployment in the product release process, modification of configuration, contract setting, cloud adaptation and visual output of real-time states in product operation, for example: alarms, maintenance of network conditions, maintenance of node device health status, etc.

The platform product service layer provides basic capabilities and implementation frameworks of typical applications, and developers can complete the blockchain implementation of business logic based on the basic capabilities and the characteristics of the superposition business. The application service layer provides the application service based on the block chain scheme to the business participants for use.

The user terminal 10a, the user terminal 10b, the user terminal 10c, and the like may each include: smart phones, tablet computers, notebook computers, palm computers, mobile internet devices (mobile internet device, MID), wearable devices (e.g., smart watches, smart bracelets, etc.), smart voice interaction devices, smart home appliances (e.g., smart televisions, etc.), outdoor electronic display devices, elevator car display devices, vehicle-mounted devices, and other electronic devices with video/image playing functions. As shown in fig. 1, the user terminal 10a, the user terminal 10b, the user terminal 10c, and the like may respectively make network connection with the server 10d, so that each user terminal may perform data interaction with the server 10d through the network connection.

It should be noted that the user terminal may include a user terminal used by an object to which data is to be delivered, or may include a user terminal used by an object to which data is to be delivered. For example, if the data to be delivered is an advertisement, the object to which the data is delivered may be an advertiser, and the server may be a server corresponding to the advertisement delivery platform selected by the advertiser. The object to which the data is put may be an object corresponding to the usage server.

Illustratively, the advertiser may send advertising material to the server via its user terminal (e.g., user terminal 10 c) in use. After receiving the advertisement material, the server may deliver an advertisement corresponding to the advertisement material to an object (e.g., a first user) corresponding to the use server. In this manner, the first user may obtain the served advertisement via the user terminal (e.g., user terminal 10 a) that he is using.

When the user terminal is a user terminal used by an object to which data is put, the user terminal should be provided with an application client or a function module for receiving the data put. The application client may include, but is not limited to: browsing clients, map clients, multimedia clients (e.g., short video clients, live video clients, video clients), entertainment clients (e.g., game clients).

Referring to fig. 2, fig. 2 is a schematic view of a scenario of data delivery according to an embodiment of the present application. As shown in fig. 2, the object data may include, but is not limited to: instant data, behavioral data, social data, and attribute data. The user terminal 10a used by the object may acquire the object data and upload the object data to the server 10d. The server 10d may acquire relevant information of the object (e.g., a put value, a hobbies, etc., not shown in fig. 2) based on the object data of the object. After the server 10d receives the request for delivering the data, the data may be delivered to the user terminal 10a of the object, so that the object can acquire the data through the user terminal 10a or other user terminals.

The instant data may include related data of the application client that the object is currently using, for example, keywords currently searched, browsed contents, etc. The behavior data may include shopping records, browsing records, etc. of the object that was previously using the application client. Social data may include social relationships in objects and networks or real life, etc. The attribute data may include attributes of the object, such as name, age, gender, contact address, common address, hobbies, etc.

The object data of the object may be divided into instant event data (e.g., instant data) that occurs currently and historical event data (e.g., behavior data, social data, attribute data) that occurs previously according to the time when the event occurs. When the historical event is a release data event, the object data of the object may further include historical operation data of the object for the historical release data, and release object data and release scene data corresponding to the historical operation data.

Wherein, the historical delivery data may be data previously delivered to the subject. The historical operational data may include operational data that the object performed on the historical impression data after the historical impression data was obtained. For example, clicking or browsing the historical delivery data, collecting, recharging, renewing, purchasing or purchasing the product corresponding to the historical delivery data, forwarding the historical delivery data or the product link corresponding to the historical delivery data to other objects, and the like. Under the condition of no specification, the operation data in the application are historical operation data, the feedback object is a historical feedback object, and the throwing data are historical throwing data.

The delivery object data may include identification information of the delivery object and data information of the delivery. For example, the delivery object is an advertiser, and the delivery object data may include an advertiser identifier, a delivery material resource identifier, and the like. The delivery scenario data may include identification information of the delivery device, content of the display data, and the like. Such as a server identification or platform identification, a device identification of a user terminal for delivering the device, an identification of display data, etc. The above identification information may be a character string including at least one character of a number, a letter, and a symbol.

In an embodiment of the present application, the object attribute set of the object may include the above-described object data, or may include object data other than operation data. It should be noted that some properties in the object data set of an object may change over time.

Referring to fig. 3, fig. 3 is a flowchart of a data processing method according to an embodiment of the application. It will be appreciated that the data processing method may be performed by a computer device, which may be a server (e.g. the server 10d in the embodiment corresponding to fig. 1), or a user terminal (e.g. any user terminal in the user terminal cluster shown in fig. 1), without applying for limitation thereto; the data processing method may include the following steps S101 to S103:

Step S101, in response to a scanning operation for a unique identification code carried by the target data, displaying an information presentation page associated with the target data: the target data is displayed in a terminal screen of the public device.

The application is not limited to targeted data, which may be advertisements, such as streaming television advertisements (also referred to as OTT (over-the-top) advertisements, which refer to devices or services that deliver digital content to a television or similar device) that are delivered to viewers in video content. The target data may alternatively be any deliverable data, such as video, audio, push information, etc. The target data may be either the first data to be delivered or the data with poor delivery effect. The poor delivery effect may be that a difference between a benefit generated after delivering data and a delivery cost is smaller than a threshold, or that the number of operation data received after delivering data is smaller than a threshold, or that an interest value corresponding to the operation data received after delivering data is smaller than a threshold, or the like. The above three thresholds may be equal or different, and are not limited herein.

In the data throwing scene, target data can be thrown to a mobile terminal or a non-mobile terminal, wherein the mobile terminal can be personal electronic equipment such as a mobile phone, a tablet and the like, and the non-mobile terminal can be public electronic equipment such as elevator room display equipment, a television and outdoor electronic display equipment and the like. The embodiments of the present application are described by taking the example that the target data is delivered to a non-mobile terminal (e.g., a public device).

When the target data is put into the public equipment (can be understood as the terminal equipment in the public area), the target data can be displayed in a terminal screen of the public equipment, and the target data displayed to the public equipment can also carry a unique identification code; the unique identification code can be an identification code with uniqueness aiming at the public equipment, for example, the unique identification code can carry the identification of the public equipment and is used for uniquely identifying the target data put into the public equipment, and different unique identification codes can be distinguished through the identification of the public equipment, so that the source tracing can be carried out on the target data put into different public equipment; the unique identification code may be a two-dimensional code, a bar code, etc., and the present application is not limited thereto.

When a user is interested in target data put in public equipment, the user can scan the unique identification code through computer equipment (such as a mobile phone) used by the user, and the computer equipment at the moment can respond to the scanning operation aiming at the unique identification code to display an information display page associated with the target data in a terminal screen of the computer equipment; for ease of understanding, the user using the computer device may be referred to as the target user. The information display page can comprise detailed description information of the target data, for example, when the target data is advertisement data of the article A, the information display page can be a purchase page of the article A and can contain detailed description of the target data; the target user can browse, collect and purchase the article A in the information display page.

When the target data is put into different public devices, each public device can display the target data, but the unique identification codes displayed in the different public devices are different, and when the target data is put into a mobile terminal, the unique identification codes do not need to be displayed in the mobile terminal. Referring to fig. 4A, fig. 4B, and fig. 4C, fig. 4A, fig. 4B, and fig. 4C are schematic diagrams of a scene of advertising provided by an embodiment of the present application. The advertisement image 401 in fig. 4A may be put in a display interface of a mobile phone (mobile terminal) of the user 1. The advertisement image 402 in fig. 4B is placed in the display interface of the smart tv in the user 2 home, and the advertisement image 404 in fig. 4C is placed in the display interface of the smart tv in the elevator car where the user 3 may sit. The user terminals corresponding to fig. 4B and fig. 4C are used as public devices, and the user 2 and the user 3 can scan the two-dimensional code image 403 in fig. 4B or the two-dimensional code image 405 in fig. 4C through a mobile phone, and display an information display page in the mobile phone, where the information display page can display detailed information of the put-in data, so that operations of clicking, purchasing, collecting, forwarding, and the like are performed in the information display page. Wherein, two-dimensional code image 403 and two-dimensional code image 405 are both unique identification codes.

Optionally, in the case of authorizing the face recognition or voice recognition operation, when the target data is put into the public device, the user (for example, user 2) may perform operations such as clicking, purchasing, collecting, forwarding, etc. on the put target data based on the face recognition or voice recognition, so that user operations may be reduced, and user experience may be improved.

Step S102, object operation data and object attribute information associated with the information display page are obtained, and delivery object data and delivery scene data associated with the unique identification codes are obtained.

Specifically, when the target user performs operations such as clicking, purchasing, collecting, forwarding, etc. on the target data or the object indicated by the target data in the information display page, the computer device may determine the operation of the target user in the information display page as object operation data, and determine the attribute information of the target user as object attribute information, where the object attribute information may include, but is not limited to, information such as a user identifier, a user tag, a consumption habit, etc.

The computer device may further obtain delivery object data and delivery scene data associated with the unique identifier, where the delivery object data may include information such as an advertiser identifier, a delivery material resource identifier, and the delivery scene data may include information such as a platform identifier, a delivery device identifier (i.e. an identifier of a public device), an advertisement material unique identifier (e.g. an advertisement material two-dimensional code), and the like.

Optionally, in the data delivery scenario, for the same target data, single-ended delivery may be performed, or information flow delivery may be performed, which is not limited in the present application. Single-ended delivery refers to single delivery of any user terminal used by a user; the information stream delivery refers to delivery of all user terminals used by a user, and operation data of the user on each data delivered, and delivery object data and delivery scene data corresponding to the operation data are collected through each user terminal. In turn, a decision may be made based on the above information as to whether to continue throwing the data or other data into the reference object.

Referring to fig. 5A, fig. 5A is a schematic diagram illustrating comparison of coverage rate of a single guest (UV) in single-ended delivery and information stream delivery according to an embodiment of the present application. As shown in fig. 5A, the UV coverage with the information stream delivery data is greater than the UV coverage with the single-ended delivery data. It can be understood that by carrying out information flow delivery data across media and screens, the coverage rate of the delivery data can be improved by utilizing the lower contact ratio between the platforms.

Referring to fig. 5B, fig. 5B is a schematic diagram illustrating comparison of acquisition costs (cost per unique visitor, CPUV) of a single visitor during single-end delivery and information flow delivery according to an embodiment of the present application. As shown in fig. 5B, the CPUV with information stream delivery data is less than the CPUV with single-ended delivery data. It can be understood that after information flow data is put through cross media and cross screens, operation data of objects aiming at the put data can be obtained through different user terminals, so that whether the put data is continued or not is determined, repeated arrival of the put data is reduced, and CPUV (Central processing Unit) can be reduced.

Step S103, object operation data, object attribute information, object data and scene data are packaged into data to be uplinked with association relation, the data to be uplinked is stored, and the data to be uplinked is used for determining the object to be launched of the target data in the subsequent scene.

Specifically, the computer device may correlate the object operation data, the object attribute information, the object data, and the scene data to obtain the data to be uplinked, and then may submit the data to be uplinked to the blockchain network, and store the data to be uplinked in the blockchain network, that is, store the data to be uplinked to the blockchain.

In one possible example, the computer device may set an association relationship among the object operation data, the object attribute information, the delivery object data, and the delivery scene data, to obtain association data; carrying out hash operation on the associated data to obtain hash data corresponding to the associated data, and packaging the hash data into data to be uplink; writing the data to be uplink into the data block, and broadcasting the data block in the block chain network so as to enable the consensus node in the block chain network to perform consensus operation on the data block; if the data block consensus passes, the data block is added to the blockchain.

After object operation data of a target user in an information display page and object attribute information of the target user are obtained, the object operation data, the object attribute information and the release object data and release scene data which are associated with the unique identification codes can be associated to generate associated data to be stored. The data format of the associated data may be in the form of key-value (key-value pair). Specifically, the computer device may generate a key element in a key value pair according to the delivery object data and the delivery scene data, and generate a value element in the key value pair according to the object operation data, and associate the key element with the value element, and may generate association data.

After generating the associated data, if the computer device does not belong to a blockchain node in the blockchain network, the computer device may upload the associated data to the blockchain node in the blockchain network over the network connection to indicate that the associated data is written into the data block. The blockchain node is a data processing node in the blockchain network, and can be used for receiving and processing externally transmitted data.

Optionally, assuming that the computer device is a blockchain node in the blockchain network, the computer device at this time may perform a series of processing on the associated data to obtain data to be stored (i.e. to-be-uplink data), and then send the data to be stored to a consensus node in the blockchain network to perform a consensus operation, and after the consensus is completed, may write the target data into the data block. Or the above processing can be performed on a plurality of associated data received in a preset time period, and then the associated data are written into the data block together.

The series of processes described above may include: and carrying out hash operation on the associated data to obtain hash data corresponding to the associated data, further writing the hash data into a data block, and broadcasting the data block in a block chain network so that a consensus node in the block chain network carries out consensus operation on the data block. After the data block is subjected to the consensus verification, each consensus node can broadcast the consensus verification result so that the consensus nodes in the block chain network can acquire the consensus verification result of the rest consensus nodes on the data block. Wherein, the consensus node can be a blockchain node participating in the consensus process in the blockchain network; the consensus verification result is used for indicating whether the consensus node approves the data block, and when the consensus verification result indicates that verification is passed, the consensus node corresponding to the consensus verification result approves the data block; when the result indicates that the verification fails, it indicates that the data block is not approved by the consensus node corresponding to the result.

After the computer device obtains the consensus verification result of the rest consensus nodes, the number of the consensus nodes passing the verification can be counted, if the number is smaller than or equal to a number threshold (determined by a consensus algorithm used in the blockchain network and generally associated with the number of the consensus nodes in the blockchain network), the data block fails to pass the consensus, and the associated data is required to be submitted again. If the number is greater than the number threshold, indicating that the data block is through consensus, the data block may be added to the blockchain, i.e., the associated data is successfully uplink.

The consensus nodes are nodes participating in consensus work in the blockchain network, and the number of the consensus nodes participating in consensus can be more than 6. The main purpose of the consensus node is to verify the authenticity of the data delivery effect. Consensus algorithms used by consensus nodes to perform consensus operations include, but are not limited to: proof of work (PoW), proof of equity (PoS), mix of proof of equity and proof of equity (pow+pos), proof of equity authorization (delegated proof of stake, DPoS), practical bayer fault-tolerance algorithm (practical byzantine fault tolerance, PBFT), rayleigh consensus protocol (ripple consensus protocol, RCP).

The hash algorithm adopted in the hash operation can compress the data or the information into the abstract, so that the format of the data can be fixed, and the data quantity can be reduced. The hashing algorithm may include, but is not limited to: SHA-1, SHA-224, SHA-256, SHA-384, and SHA-512. Taking the SHA-256 algorithm as an example, the SHA-256 algorithm can generate a 256-bit hash value for any size data, and can generate different hash values for different data.

Optionally, the computer device may store the associated data in a chained hash table, and may store, according to a result of the hash operation, the object operation data, the object attribute information, the associated data corresponding to the put object data and the put scene data on the same hash chain in the chained hash table. The chain hash table includes hash chains respectively corresponding to a plurality of operation data, delivery object data, and delivery scene data. The blockchain node can transmit key elements in the associated data into a hash function, and the hash function determines which hash chain the associated data corresponds to and a specific position in the hash chain in a hash manner. For example, a hash function is defined that maps key k to position n in the chain hash table. n is referred to as k, expressed as: h (k) =n. The purpose of this hash function is to distribute the key elements as evenly and randomly as possible into the chain hash table.

It should be noted that, in one or more embodiments, a user who generates operation data in the information presentation page may be referred to as a feedback object corresponding to the target data, that is, the target user may be referred to as a feedback object of the target data, and the feedback object may refer to a user who performs a related operation on the target data. Before targeting data is targeted, a targeting object (e.g., an advertiser) may determine a device that is appropriate for targeting data or a reference object for targeting data. The reference object may be a launched object screened for the target data, where the reference object may include a feedback object that performs an operation on the launched target data (i.e., generates operation data), or may include a user that does not perform an operation on the launched target data; of course, the feedback object may belong to the reference object, or may be a user associated with the reference object. For example, after the reference object acquires the thrown target data, the link corresponding to the target data may be forwarded to another object (user), and after the object performs operations such as clicking or browsing, collecting, recharging, renewing and the like on the target data, the object may be referred to as a feedback object that performs related operations on the target data.

Wherein, the determining process of the reference object may include: candidate objects which may be suitable for throwing the target data may be initially screened, and the candidate objects may be objects in which the server can throw data, may be understood as objects in which throwing data of a platform corresponding to the rejection server is not set, or may be objects in which throwing data related to the rejection target data is not set. Alternatively, the candidate object may be an object recorded in a data management platform (data management platform, DMP).

It can be understood that after the reference object corresponding to the target data is determined, the target data can be put into the reference object, so as to improve the accuracy of the put data. It should be noted that the number of the reference objects and the number of the candidate objects may be plural, and the number of the candidate objects should be greater than or equal to the number of the reference objects, for example, the target user belongs to the reference object corresponding to the target data and also belongs to the feedback object corresponding to the target data.

The method for acquiring the reference object is not limited, the throwing range of the target data can be determined based on the attribute information of the target data, and the user in the throwing range can be determined as the reference object. Therefore, the reference object is determined by the attribute information of the target data, and the accuracy of throwing the target data into the reference object is improved.

The attribute information of the target data may include identification information, data type, product type, applicable group, etc. of the target data. For example, the target data is an advertisement of an english learning course, and the identification information may be a character string corresponding to the advertiser and english. The data type may be advertising and the product type may be lesson. The applicable population may be 20-40 years old, and people related to English improvement have recently been browsed, and the like.

The attribute information of the target data may further include delivery budget, number of delivery times or delivery frequency, delivery labels, number of coverage objects, and other delivery requirements of the target data. The put tag may be a summary or a keyword corresponding to the attribute information. For example, if the target data is an advertisement of an english learning course, the delivery label may be "english", "foreign trade", or the like. The number of overlay objects refers to the number of objects that may receive the delivery target data. It can be understood that the charging standards of different platforms are different, and when the different platforms perform data delivery, corresponding fees need to be paid. Thus, before an object throwing data throws target data through the platform, a throwing request of the target data is submitted to the platform. The server of the platform can determine the delivery scheme of the target data based on the charging rule of the platform and the delivery requirement in the delivery request, and can also send the delivery scheme to the object of the delivery data. Alternatively, the server may determine a reference object satisfying the delivery requirement based on the transceiving rule, and may deliver the target data to the reference object.

In one possible example, the reference object includes a first object and a second object; the method of acquiring the reference object may include the steps of: acquiring a tag set of target data and the number of coverage objects according to attribute information of the target data, and acquiring the tag set of each candidate object in a plurality of candidate objects; and if the tag set of the candidate object comprises the tag set of the target data, taking the candidate object as a first object, and if the number of the first object is smaller than the number of the coverage objects, performing object expansion based on the tag set of the first object to obtain a second object.

Wherein the attribute information of the target data may be carried in a delivery request of a delivery object (e.g., advertiser, etc.). Optionally, before the method of acquiring the reference object, a launch request to launch the object is received. The drop request is for requesting to drop target data. The delivery request may include attribute information of the target data as described above. Or includes identification information of the target data, through which the computer device can determine the target data and acquire attribute information of the target data.

The tag set includes at least one tag. The tag set of the target data includes at least one put tag of the target data, and the tag set of the candidate object includes at least one object tag of the candidate object. The description of the attribute information, the candidate objects, the delivery labels, and the number of the coverage objects may refer to the foregoing or the following, and will not be repeated here.

The method for acquiring the object tag is not limited by the application, and the object tag can be a tag which is self-defined by a candidate object, for example, a user can select a game from interesting data types as the tag of the user before using an application. Or the object attribute set and the operation data set of the candidate object may be acquired first. And analyzing object labels of the candidate objects based on the object attributes and the operation data of the candidate objects and the put object data and the put scene data corresponding to the operation data.

When the tag set of the candidate object includes the tag set of the target data, the candidate object may be regarded as the first object. That is, the tag set of the first object includes the tag set of the target data. The second object refers to a candidate object selected in the process of expanding the number of the first objects. The sum between the number of first objects and the number of second objects is equal to the number of overlay objects. That is, the second object may be a candidate object obtained by object expansion based on the difference between the number of overlay objects and the number of first objects and the tag set of the first object.

The object expansion method can adopt a similar crowd expansion (lookalike) algorithm, or can search for associated objects based on a knowledge graph of social relations. The alookalike algorithm is a term in calculating advertisements, and does not refer to a certain algorithm alone, but is a generic term of a class of methods. The purpose is to achieve an expansion in the number of similar objects. The algorithm reasons of the alookalike algorithm mainly include the following three,

First, population diffusion is performed using object attributes. For example, seed users are labeled, and the same label is used to find the target population.

Second, population diffusion is performed using a classification model. For example, the seed user is taken as a positive sample and the candidate is taken as a negative sample. And screening all candidate objects by training a classification model to obtain objects similar to the seed users.

Thirdly, the social network is utilized to carry out crowd diffusion, for example, the friend relationship of the seed user is utilized to transmit the labels to friends in the community, so that crowd diffusion is realized.

It will be appreciated that a second object similar to the first object is looked up based on a look-up algorithm such that the sum of the number of first and second objects is equal to the number of overlay objects. In this way, the number of reference objects can be ensured, and the similarity between the reference objects can be ensured.

The application is not limited to the specific content of the lookalike algorithm and the object expansion algorithm. The following is an illustration of a first one of the lookahead algorithms with a first object as a seed user. Illustratively, assume that the overlay object data of the target data is 5000w, the tag set { English, foreign }, of the target data. If the number of the first objects including the tag set of the target data is 1000w, the server may find similar tag sets based on the tag set of the first objects, to obtain { english, learning, foreign trade, two-line city }. And searching 4000w second objects based on the similar label set.

It should be noted that the number of objects that completely match the set of similar tags may be less than the difference between the number of overlay objects and the number of first objects. The second objects may be selected in order from large to small based on matching values between the similar tag sets and tag sets of candidate objects other than the first object and sorting the matching values. The matching value here may be obtained by a similarity value between the similarity tag and the object tag.

Optionally, after the method of acquiring the reference object, target data may be put on the reference object (i.e., the first object and the second object). The first object is obtained by selecting the tag set of the target data and the tag set of the object, the second object is expanded based on the tag set of the first object, and after the target data is put into the first object and the second object, the accuracy of putting the target data into the reference object can be improved, and the reference of the feedback object to the operation data of the target data can be improved.

Referring to fig. 6, fig. 6 is a schematic flow chart of an advertisement delivery method according to an embodiment of the present application. As shown in FIG. 6, the advertisement delivery method may include the following steps S301-S306.

Wherein, step S301: the advertiser sends a request for placement of advertising material to a server (of the platform), the request for placement including a placement requirement. Specifically, the advertisement material to be put can be selected for the advertiser, then the advertisement putting requirement corresponding to the advertisement material is filled in or selected, and the 'submit' is clicked, so that the putting request of the advertisement material is sent to the server.

After the server receives the delivery request, step S302 may be performed: and determining a delivery range according to the delivery requirements. The delivery scope includes users (or objects) that meet the delivery requirements. The server may perform step S303 before delivering the advertisement to the users within the delivery scope: and generating advertisement materials corresponding to different user terminals. For example, the advertisement material of the mobile phone is an original material, and the advertisement material of the intelligent television in the elevator cabin is an advertisement material carrying a scene identifier, so that the operation data in the public area can be monitored and traced.

Step S304: the server delivers corresponding advertisement materials to at least two user terminals of users in the delivery range.

Step S305: the user terminal sends operation data (for example, clicking times, watching times and the like) of the advertisement materials, object attributes (for example, user identification, user labels, consumption habits and the like) of the user, and uplink requests of advertiser data (for example, advertiser identification, material resource delivery identification and the like, which are equivalent to delivery object data described in the application) and delivery scene data (for example, platform identification, delivery equipment identification, advertisement material two-dimensional codes and the like) corresponding to the operation data to the blockchain.

Step S306: and the blockchain stores the operation data of the user on the advertisement materials, the object attribute of the user, and the advertiser data and the delivery scene data corresponding to the operation data in a uplink manner. In this way, the authenticity and validity of the data can be guaranteed through the blockchain.

The above-mentioned ul storage procedure may be referred to the description in step S103 of the embodiment corresponding to fig. 3, and will not be described herein.

In one possible example, after step S306, the server may generate an LTV (life time value) model based on the relevant data of each user stored in the blockchain; and delivering the advertisement materials to be delivered to the corresponding user terminals according to the LTV model.

LTV is the sum of the total revenue that the product receives from the user to loss. The LTV is used for measuring the value of a user on a product, is a measurement index of a bias period, and can be used for improving the product or a delivery strategy. The time and value model corresponding to the LTV is used to measure how much of the total value gain an object contributes to a platform or enterprise throughout a product cycle (or within a time period).

Fig. 7 is a schematic diagram illustrating an LTV model according to an embodiment of the application. As shown in fig. 7, the LTV curves in the LTV model can be divided into a review period, a formation period, a stabilization period, and a degradation period according to time variation for describing the value created by the subject in recognizing discarding of a product.

There are 3 ways to calculate the LTV as follows:

LTV＝LT*ARPU (1)

where LT is the average lifecycle and ARPU is the average user benefit (average revenue per user, ARPU). For example, LT is 3 months and ARPU is 10 yuan/month, ltv=3×10, i.e. 30 yuan.

LTV=N days running water/N days newly added (2)

Wherein N can be custom, e.g., 30 days, 60 days, 90 days, etc. N may also be determined based on the length of time corresponding to the user retention. For example, the user retention is 1% for a corresponding length of time, typically 60 days.

Ltv=user lifecycle ARPDAU (3)

y＝a*x^b (4)

Wherein ARPDAU is the average per-pay user benefit. x is the number of days of use and a and b are coefficients of the LTV model. Since the user retention rate in the first N days decays rapidly, a stable decay convergence sequence appears, and based on the retention function (see formula (4)), x can be respectively 2, 7, 14, 30, 180 and the like, and then the weighting coefficients corresponding to the above x are selected for calculation, so as to obtain the life cycle of the user. After calculation of the user lifecycle, the LTV value is multiplied by the average pay-per-user benefit (average revenue per paying user, ARPDAU).

Optionally, at least one of the above 3 calculation modes may be adopted to quantize the operation data of each data put in, to obtain the LTV value of each data. And calculating the throwing value of the object based on the LTV value of the object to each data.

The first value may be a ratio between the LTV and the user acquisition cost (customer acquisition cost, CAC) or CPUV, and the like, and is not limited herein. In case the ratio between LTV and CAC (or CPUV) is greater than 1, it indicates that there is a benefit and the delivery can be continued. Otherwise, the delivery is not continued.

Therefore, the method manages the released data in a cross-media frequency control mode, and can acquire the operation data of the user, so that the exposure and interest excitation can be pushed through the flow and the content, the marketing closed loop of the released data link is realized, the invalid release can be reduced, and the release cost is reduced.

In the embodiment of the application, for the target data put in the public equipment, the target data can carry a unique identification code, an information display page associated with the target data can be displayed by scanning the unique identification code, and the object operation data generated in the information display page and the object attribute information corresponding to the objects for generating the object operation data can be acquired; and the unique identification code can be used for associating the put object data corresponding to the target data with the put scene data, so that the data can be associated and stored in the blockchain, namely, the related data of the put target data can be effectively obtained, the tracing of the target data is realized through the blockchain, the management effectiveness of the put data can be improved, and the data put cost is reduced.

The user who performs the operation on the target data put in may be referred to as a feedback object, and operation data and object attributes corresponding to all feedback objects may be stored in a blockchain, where the data stored in the blockchain may be used to determine the put object of the target data in the subsequent put scene. The following describes the delivery process of the target data in the subsequent delivery scenario by means of fig. 8.

Referring to fig. 8, fig. 8 is a flowchart illustrating a target data delivery processing method according to an embodiment of the present application. In the embodiment of the present application, the target data delivery processing method may be executed by a computer device, which may be a server (for example, the server 10d in the embodiment corresponding to fig. 1), or a user terminal (for example, any one of the user terminals in the user terminal cluster shown in fig. 1), and the application is not limited thereto. As shown in fig. 8, the delivery processing method of the target data may include the following steps S501 to S505, where:

step S501: and acquiring an operation data set of the target data from the block chain, determining a plurality of feedback objects corresponding to the target data according to the operation data set of the target data, and acquiring object cluster characteristics of the plurality of feedback objects.

Specifically, after the target data is put in, the operation data of the object that has feedback to the target data may be (uplink) stored, so as to obtain an operation data set of the target data, where the operation data set may include the object operation data corresponding to the target user. In the embodiment of the present application, the feedback object refers to an object having feedback to the target data, that is, the feedback object is an object of the operation data in the operation data set of the target data. The object cluster characteristics of the feedback objects refer to common or mostly similar characteristics in the object clusters corresponding to the feedback objects.

The following is an illustration of one feedback object of the plurality of feedback objects, and in one possible example, the method of obtaining object cluster characteristics of the plurality of feedback objects may include the steps of: acquiring operation data of a feedback object on the first data, and acquiring a preference value of the feedback object on the first data according to the operation data of the feedback object on the first data; acquiring target operation data from operation data of the feedback object on the first data according to the preference value of the feedback object on the first data, and acquiring target object attributes corresponding to the feedback time of the target operation data from an object attribute set of the feedback object; acquiring object preference characteristics of a feedback object according to the object throwing data, the scene throwing data and the object attribute corresponding to the target operation data; and acquiring the object weight of the feedback object and the data weight of the first data, and acquiring object cluster characteristics of a plurality of feedback objects according to the object weight and the object preference characteristics of the feedback object and the data weight of the first data.

The first data refers to data fed back by the feedback object. It should be noted that, the first data of each feedback object may be the same or different, but the first data of each feedback object includes the target data. The number of first data may be 1 (i.e., target data), or more than 1.

The preference value of the feedback object for the first data is used to describe the satisfaction of the feedback object with the first data. The operation performed on the first data may be determined by the feedback object, for example, the preference value corresponding to the purchase operation is 1, the preference value corresponding to the purchase operation is 0.8, the preference value corresponding to the click operation is 0.5, the preference value corresponding to the browse operation is 0.3, and so on.

In one possible example, the operation data of the feedback object on the first data includes the first operation data of the feedback object on the first data and the second operation data of the item corresponding to the first data; the method for obtaining the preference value of the feedback object to the first data may include: and acquiring a preference value of the feedback object to the first data according to the first operation data and the second operation data.

The first operation data is operation data of the feedback object on the first data, and the second operation data is operation data of the feedback object on the article corresponding to the first data. For example, if the item is a game piece, the operational data for the item may include frequency, duration, etc. of use of the game piece. If the item is food, the operation data for the item may include comment information, number of repurchase, and the like.

It may be appreciated that, in this example, the preference value of the feedback object for the first data may be obtained from both the operation data performed by the feedback object for the first data and the operation data performed for the item corresponding to the first data, and the accuracy of obtaining the preference value may be improved.

In the embodiment of the present application, the target operation data may be operation data in which a preference value in operation data of the feedback object on the first data is greater than a threshold value. The threshold value is not limited here, and the determination may be made based on the number of operation data sets of the first data, or the data weight of the first data, or the like. The target object attribute may be an object attribute corresponding to a feedback time of the target operation data in an object attribute set of the feedback object.

The delivery object data and the delivery scene data corresponding to the operation data may refer to the foregoing related description, and will not be described herein. The object preference characteristics of the feedback object include at least one characteristic of the feedback object preference type. For example, if the preference type is color, the object preference feature is blue. If the preference type is music, the object preference feature is tempo. If the preference type is a speaker, the object preference feature is star. If the preference type is a scene, the object preference feature is elevator. If the preference type is a put object, the object preference feature is a game or the like.

It can be understood that the accuracy of analyzing the object preference can be improved by selecting the target operation data according to the magnitude of the preference value. And selecting a target object attribute corresponding to the feedback time of the target operation data, and acquiring object preference characteristics of the feedback object according to the released object data, the released scene data and the target object attribute corresponding to the feedback time of the target operation data, so that the object preference characteristics of the feedback object are acquired by analyzing the object data, the released object data and the released scene data of the feedback object at the moment, and the accuracy of acquiring the object preference characteristics can be improved.

In the embodiment of the application, the object weight of the feedback object is used for describing the influence value of the object preference feature of the feedback object on the object cluster feature. The data weight of the first data is used to describe an impact value of the first data on the object cluster feature. The object cluster features of the feedback objects can be obtained by weighting the object preference features of the feedback objects to the first data through the object weights of the feedback objects and weighting the numerical values obtained through the weighting of the data weights of the first data.

It can be understood that the object preference characteristics of the feedback objects are obtained through the object attribute sets of the feedback objects corresponding to the target data and the operation data of the feedback objects on the first data. And then the object preference characteristics, the object weights of the feedback objects and the data weights of the first data are used for obtaining the object cluster characteristics of a plurality of feedback objects, so that the accuracy of obtaining the object cluster characteristics can be improved.

In one possible example, a method of acquiring an object weight of a feedback object and a data weight of first data may include the steps of: acquiring a first number of operation data sets of a feedback object and a first value of the feedback object; acquiring a second number of operational data sets of the first data and a second value of the first data; acquiring object weights of feedback objects according to the first quantity and the first valence; and acquiring the data weight of the first data according to the second quantity and the second value.

Wherein the first value may be a put value, e.g., an LTV value, of putting data to the feedback object; the second value may be a value of the put first data. The acquisition may be based on a difference or ratio between the payout and the return of the first data. The first number is the number of data sets of the feedback object's operational data on the first data. The second number is the number of data sets of operational data of the first data, the operational data sets of the first data including operational data of the feedback object on the first data, and may also include operational data of other objects on the first data. The second number is greater than or equal to the first number.

The object weight of the feedback object may be a weighted value of the first number and the first value, or may be a product between a value corresponding to the first number and the first value, or the like. The data weight of the first data may be a weighted value of the second number and the second value, or may be a product of a value corresponding to the second number and the second value, or the like, which is not limited herein.

It will be appreciated that the greater the number of sets of operational data for a feedback object, the greater the feedback events representing the data that the feedback object is engaged in delivering, the greater the referential of the operational data for the feedback object. The greater the first value of the feedback object, the more likely the benefit of delivering data to the feedback object. The greater the number of operational data sets of the first data, the greater the number of objects representing feedback events involving the first data, and the greater the references to the operational data sets of the first data. The greater the put value of the first data, the greater the referential of the operational data set representing the first data. In this example, the object weight of the feedback object is obtained according to the number of operation data sets of the feedback object and the value of the feedback object, and the data weight of the first data is obtained according to the number of operation data sets of the first data and the value of the first data, so that the accuracy of obtaining the weight can be improved.

Step S502: the method comprises the steps of obtaining object characteristics of each object to be selected in an object set to be selected, and determining the object to be put from the object set to be selected according to the object characteristics and the object cluster characteristics of each object to be selected.

The object set to be selected may include candidate objects other than feedback objects, and the candidate objects may refer to the related description, which is not described herein. The object characteristics of the object to be selected may refer to the description of the object preference characteristics of the feedback object. The object to be put is an object selected from the objects to be selected, and the method for selecting the object to be put can select the matching values of various dimension characteristics between the object characteristics of the object to be selected and the object cluster characteristics. For example, a matching value is greater than a threshold value, or a weighted value of various matching values is greater than a threshold value, the object to be selected is selected as the object to be put in, etc., where the threshold value is not limited.

The object to be selected may be an object of which the preference value of the feedback object to the target data is greater than a threshold value and the target object corresponding to the target data is not purchased. The threshold value is not limited here. It can be understood that the target data is put again to the object which does not purchase the target object corresponding to the target data and has a certain preference to the target data, so that the probability of purchasing the target object by the feedback object can be improved.

Step S503: and acquiring an operation data set of the object to be put from the blockchain, and acquiring the data set characteristics of the object to be put according to the operation data set of the object to be put.

The operation data set of the object to be put can be obtained from a blockchain, and can comprise operation data of the object to be put on each piece of put data. In one possible example, identity information of the delivery object of the target data may be verified. Upon verification pass, the representation may target data with reference to the operational data set of the object to be placed. And acquiring a part of operation data set of the object to be put or a part of operation data set of the object to be put according to the protocol information between the computer equipment and the blockchain. In this way, the security of the data can be improved.

In another possible example, a request for a to-be-launched object to uplink operation data of the second data may be received; acquiring the throwing value of the object to be thrown according to the object attribute set of the object to be thrown, the historical operation data and throwing object data corresponding to the historical operation data; and if the throwing value of the object to be thrown is larger than a second threshold, performing uplink storage on the operation data of the object to be thrown on the target data, and throwing object data and throwing scene data corresponding to the operation data.

The second data are feedback data of the object to be put. The second data and the first data are different. The number of second data may be 1 or more than 1. In this example, the operation data of the object to be put on the second data is data to be uploaded to the blockchain, and the history operation data is operation data of the object to be put on the second data that has been uploaded to the blockchain.

The method for obtaining the delivery value of the object to be delivered may refer to the foregoing or the following, or in one possible example, the step of obtaining the delivery value of the object to be delivered may include the steps of: acquiring reference values of object clusters of a plurality of feedback objects; acquiring an interest value of the object to be put on the second data based on the historical operation data of the object to be put on and the put object data of the object to be put on; and acquiring the throwing value of the object to be thrown based on the reference value and the interest value.

Wherein the reference value of the object clusters of the plurality of feedback objects may be determined by the consumption levels of the plurality of feedback objects. Or may be determined by a similarity value between a preference data type of the feedback object and a data type of the target data in the object cluster, etc., without limitation. The interest value is used to describe the degree to which the object to be delivered is interested in the second data, and reference may be made to the description of the preference value of the feedback object for the first data, which will not be described in detail herein. The delivery value may be equal to a weighted value between the reference value and the interest value, or may be equal to a maximum or minimum value between the reference value and the interest value, etc., without limitation.

It can be appreciated that in this example, the delivery value of the object to be delivered is obtained from both the reference values of the object clusters of the plurality of feedback objects and the interest values of the object to be delivered in the second data, so that the accuracy of obtaining the delivery value can be improved, and the reference of the operation data for storing the second data can be improved.

The method is not limited to the second threshold, if the throwing value of the object to be thrown is larger than the second threshold, the operation data set of the object to be thrown has a certain referential property to throwing target data or other data, and the operation data of the object to be thrown to the second data, throwing object data and throwing scene data corresponding to the operation data can be uploaded to the blockchain. The method specifically comprises the steps of firstly carrying out hash operation on operation data of a feedback object on target data, and throwing object data and throwing scene data corresponding to the operation data to obtain hash data. And broadcasting the hash data so that the consensus node receiving the broadcast performs consensus operation on the hash data. If the hash data passes the consensus, the hash data can be stored in a uplink mode, so that operation data of the object to be put on to the second data, and put object data and put scene data corresponding to the operation data can be obtained from the block chain. And through hash operation and consensus operation, the security and the authenticity of the data can be improved.

In the embodiment of the application, the data set features are features of a data set corresponding to operation data in the operation data set of the object. In one possible example, the operation data set of the object to be put includes operation data of the object to be put on the second data; the method for acquiring the data set characteristics of the object to be put in can comprise the following steps: acquiring a preference value of the object to be put on the second data according to the operation data of the object to be put on the second data; acquiring data preference characteristics of the second data according to the object data and scene data of the second data and preference values of the object to be released on the second data; acquiring a data weight of the second data and an object weight of an object to be put in; and acquiring the data set characteristics of the object to be put in according to the data weight and the data preference characteristics of the second data.

The preference value of the object to be put on to the second data may refer to the description of the preference value of the feedback object to the first data, the data weight of the second data may refer to the description of the data weight of the first data, and the object weight of the object to be put on may refer to the description of the object weight of the feedback object, which is not described herein.

The data preference feature of the second data may first select reference data from the second data according to the preference value of the object to be put on to the second data, for example, select the second data with the preference value greater than a threshold value as the reference data, or select the first N second data with the greatest preference value as the reference data, etc. The threshold value and the N value are not limited herein. And then acquiring the data characteristics corresponding to the object data and the scene data of the reference data as data preference characteristics.

The data set characteristics of the object to be put can be obtained by weighting the data preference characteristics of the second data by the object weight of the object to be put and weighting the numerical value obtained by weighting by the data weight of the second data. In this example, the data preference characteristics of the second data are obtained from preference values of the object to be delivered for the operation data of the respective second data, as well as the delivery scene data and the delivery object data of the second data. And acquiring the data set characteristics of a plurality of second data according to the data preference characteristics of the second data, the object weight of the object to be put and the data weight of the second data, so that the accuracy of acquiring the data set characteristics can be improved.

Step S504: and obtaining a matching value between the data set characteristic of the object to be put and the data characteristic corresponding to the target data.

Step S505: and if the matching value is larger than the first threshold value, throwing target data into the object to be thrown.

The data characteristics corresponding to the target data can be determined based on the delivery object data and the delivery scene data of the target data. The matching value between the data set feature of the object to be put and the data feature corresponding to the target data can be obtained by weighting based on the matching sub-value between the features corresponding to the dimensions of each data feature. The weight of the data feature dimension can be set according to the ordering, the number and the like of various data features.

The present application is not limited to the first threshold, and the first threshold may be set based on the number of objects to be put in, the number of feedback objects, and the like. It can be understood that, if the matching value between the data set feature of the object to be put and the data feature corresponding to the target data is larger, the probability that the object to be put performs the operation on the target data is indicated to be larger, so that the target data can be put into the object to be put.

In one possible example, step S505 further includes: and if the matching value is larger than the first threshold value, target data are put on the object to be put in a putting scene of the object to be put in.

The scene of the object to be launched generally refers to a scene suitable for launching data into the object to be launched. The application does not limit the throwing scene of the object to be thrown, and can be determined by the time of using the user terminal by the object to be thrown, or further determined by the time of using the application client for the object to be thrown, and the like. It should be noted that the delivery scene is a scene range, may include multiple time points, and may include multiple sites.

In one possible example, the data processing method further comprises the steps of: acquiring a reference operation data set corresponding to target data from an operation data set of an object to be put in; and determining the delivery scene of the object to be delivered based on the delivery scene data corresponding to the operation data in the reference operation data set.

The reference operation data set is an operation data set selected from operation data sets of objects to be put in. The selection may be based on the data characteristics of the target data or the delivery object data of the target data, which is not limited herein. The delivery scene of the object to be delivered can be determined by referring to the time, place, delivery equipment and other information in the delivery scene data corresponding to the operation data in the operation data set. It should be appreciated that the delivery scene of the object to be delivered may be the one closest to the current time. In this example, a reference operation data set is first selected. And determining the delivery scene of the object to be delivered based on the delivery scene data corresponding to the operation data in the reference operation data set. Therefore, the target data is put into the object to be put in the putting scene, the accuracy of the put data can be further improved, and the improvement of the putting effect is facilitated.

In the method shown in fig. 8, an object to be put is selected from objects to be selected according to object characteristics of the object to be selected and object cluster characteristics corresponding to a feedback object having feedback on target data. And acquiring the data set characteristics of the object to be put according to the operation data set of the object to be put. And then obtaining a matching value between the data set characteristic of the object to be put and the target data characteristic corresponding to the target data. And if the matching value is larger than the first threshold value, throwing target data into the object to be thrown. That is, the object to be put in which feedback operation is possibly performed on the target data is determined according to the object cluster characteristics acquired by the operation data of the target data, and then the object to be put in which the target data is put in the next time is determined according to the matching value between the data cluster characteristics acquired by the operation data of the object to be put and the data characteristics of the target data, so that data put in is realized in a closed link, the accuracy of the data put in the next time is improved, and the improvement of the put effect is facilitated.

In one possible example, after delivering the target data to the object to be delivered, the method further comprises the steps of: acquiring target operation data of an object to be put on to target data; acquiring a preference value of an object to be put on to target data based on the target operation data; and if the preference value of the object to be put on the target data is smaller than the third threshold value and larger than the fourth threshold value, putting the target data on the object to be put on a subsequent putting scene of the object to be put on.

The target operation data may include operation data that is executed on the target data by the object to be launched after the target data is acquired. The method for obtaining the preference value of the object to be put on to the target data may refer to the description of obtaining the preference value of the feedback object to the first data, which is not described herein. The subsequent delivery scene of the object to be delivered may be the delivery scene of the object to be delivered after step S505, and may be understood as the next delivery scene.

The present application is not limited to the third threshold value and the fourth threshold value, and the third threshold value is larger than the fourth threshold value. It will be appreciated that when the preference value of the object to be put on the target data is less than or equal to the fourth threshold value, which indicates that the object to be put on is not interested in the target data and may be relatively objectionable, the target data may no longer be put on the object to be put on. When the preference value of the object to be put on the target data is larger than or equal to the third threshold value, the object to be put on is known about the target data, and possibly the target object corresponding to the target data is purchased, so that the target data can not be put on the object to be put on any more. When the preference value of the object to be put on the target data is smaller than the third threshold value and larger than the fourth threshold value, the object to be put on is interested in the target data and is not completely known, and the target data can be put on the object to be put on continuously. Therefore, whether to continue to throw the target data into the object to be thrown is determined through the preference value obtained by the target operation data of the object to be thrown, and the effect of throwing the data is improved.

In one possible example, the target operation data includes forwarding operation data obtained by forwarding the target data to the forwarding object by the object to be put, and the method may further include the steps of: acquiring a preference value of a forwarding object to target data based on forwarding operation data; and if the preference value of the forwarding object to the target data is smaller than the third threshold value and larger than the fourth threshold value, the target data is delivered to the forwarding object in the delivery scene of the forwarding object.

The forwarding object refers to an object to be launched that forwards target data or a product link corresponding to the target data, and may be any object except the object to be launched. The forwarding operation data is operation data that the forwarding object performs on target data after acquiring the target data. The method for obtaining the preference value of the forwarding object to the target data may refer to the description of obtaining the preference value of the feedback object to the first data, and the delivery scene of the forwarding object may refer to the description of the delivery scene of the object to be delivered, which is not described herein.

It will be appreciated that in this example, by deciding whether to drop target data to a forwarding object through a preference value obtained by forwarding operation data of the forwarding object, coverage of drop data may be improved.

It will be appreciated that in the specific embodiments of the present application, data (e.g., object attributes, object attribute sets, object data, instant event data, historical operation data, and corresponding delivery object data and delivery scenario data) of users such as users, enterprises, institutions, etc. may be involved, and when the above embodiments of the present application are applied to specific products or technologies, permissions or agreements of the users, enterprises, institutions, etc. are required, and collection, use, and processing of the relevant data are required to comply with relevant laws and regulations and standards of relevant countries and regions.

The foregoing details of the method according to the embodiments of the present application and the apparatus according to the embodiments of the present application are provided below.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 9, the data processing apparatus 8 includes:

the scanning module 81 is configured to display an information presentation page associated with the target data in response to a scanning operation for a unique identification code carried by the target data: the target data are displayed in a terminal screen of the public equipment;

the data acquisition module 82 is configured to acquire object operation data and object attribute information associated with the information presentation page, and acquire delivery object data and delivery scene data associated with the unique identification code;

The storage module 83 is configured to package object operation data, object attribute information, delivery object data, and delivery scene data into to-be-uplink data having an association relationship, and store the to-be-uplink data in a uplink manner; the data to be uplinked is used for determining the launched object of the target data in the subsequent launching scene.

In one or more embodiments, the storage module 83 includes:

an association setting unit 831 is configured to set an association between the object operation data, the object attribute information, the delivery object data, and the delivery scene data, to obtain association data;

the hash operation unit 832 is configured to perform hash operation on the associated data to obtain hash data corresponding to the associated data, and package the hash data into data to be uplink;

a broadcasting unit 833, configured to write the data to be uplink into a data block, and broadcast the data block in the blockchain network, so that a consensus node in the blockchain network performs a consensus operation on the data block;

a billing unit 834 for adding a data block to the blockchain if the data block consensus passes.

In one or more embodiments, the data processing device 8 may further include:

the first obtaining module 84 is configured to obtain an operation data set of target data from the blockchain, determine a plurality of feedback objects corresponding to the target data according to the operation data set of the target data, and obtain object cluster features of the plurality of feedback objects; the operation data set of the target data includes object operation data;

The second obtaining module 85 is configured to obtain an object feature of each object to be selected in the object set to be selected, and determine an object to be put from the object set to be selected according to the object feature and the object cluster feature of each object to be selected;

the third obtaining module 86 is configured to obtain an operation data set of the object to be put from the blockchain, and obtain a data set feature of the object to be put according to the operation data set of the object to be put;

the fourth obtaining module 87 is configured to obtain a matching value between a data set feature of the object to be put and a data feature corresponding to the target data;

the delivery module 88 is configured to deliver the target data to the object to be delivered if the matching value is greater than the first threshold.

In one or more embodiments, the first acquisition module 84 includes:

the first obtaining unit 841 is configured to obtain operation data of the feedback object a on the first data, and obtain a preference value of the feedback object a on the first data according to the operation data of the feedback object a on the first data; the first data comprises target data, and the feedback object a belongs to a plurality of feedback objects;

the second obtaining unit 842 is configured to obtain, according to a preference value of the feedback object a for the first data, target operation data from operation data of the feedback object a for the first data, and obtain, from an object attribute set of the feedback object a, a target object attribute corresponding to a feedback time of the target operation data;

The third obtaining unit 843 is configured to obtain the object preference feature of the feedback object a according to the delivery object data, the delivery scene data, and the target object attribute corresponding to the target operation data;

the fourth obtaining unit 844 is configured to obtain an object weight of the feedback object a and a data weight of the first data; and acquiring object cluster characteristics of a plurality of feedback objects according to the object weights and the object preference characteristics of the feedback objects a and the data weights of the first data.

In one or more embodiments, the fourth acquisition unit 844 includes:

the first obtaining subunit 8441 is configured to obtain a first number of operation data sets of the feedback object a and a first value of the feedback object a, and obtain a second number of operation data sets of the first data and a second value of the first data;

the second obtaining subunit 8442 is configured to obtain the object weight of the feedback object a according to the first number and the first value, and obtain the data weight of the first data according to the second number and the second value.

In one or more embodiments, the operational data set of the object to be put includes operational data of the object to be put on the second data; the third acquisition module 86 includes:

the fifth obtaining unit 861 is configured to obtain a preference value of the object to be put on the second data according to operation data of the object to be put on the second data;

The sixth obtaining unit 862 is configured to obtain a data preference feature of the second data according to the delivery object data and the delivery scene data of the second data, and a preference value of the object to be delivered for the second data;

the seventh obtaining unit 863 is configured to obtain a data weight of the second data and an object weight of the object to be put in; and acquiring the data set characteristics of the object to be put in according to the data weight and the data preference characteristics of the second data.

In one or more embodiments, the data processing device 8 further comprises:

the receiving module 89 is configured to receive a uplink request of operation data of the object to be put on to the second data;

the fifth obtaining module 90 is configured to obtain a delivery value of the object to be delivered according to the object attribute set of the object to be delivered, the historical operation data, and the delivery object data corresponding to the historical operation data;

the uplink module 91 is configured to store, in uplink, operation data of the object to be released on the target data, and release object data and release scene data corresponding to the operation data, if the release value of the object to be released is greater than the second threshold.

In one or more embodiments, the data processing device 8 further comprises:

a sixth obtaining module 92, configured to obtain a tag set and the number of coverage objects of the target data according to attribute information of the target data; acquiring a tag set of each candidate object in a plurality of candidate objects; if the tag set of the candidate object comprises the tag set of the target data, taking the candidate object as a first object; if the number of the first objects is smaller than the number of the covered objects, performing object expansion based on the tag set of the first objects to obtain second objects; the sum between the number of first objects and the number of second objects is equal to the number of overlay objects;

The delivery module 88 is also configured to deliver the target data to the first object and the second object.

It should be noted that the implementation of the respective modules, units and sub-units may also correspond to the respective descriptions of the method embodiments shown with reference to fig. 3 and 8.

Further, referring to fig. 10, fig. 10 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 10, the computer device 1000 may be a user terminal, for example, the user terminal 10a in the embodiment corresponding to fig. 1, or a server, for example, the server 10d in the embodiment corresponding to fig. 1, which is not limited herein. For ease of understanding, the present application takes a computer device as an example of a user terminal, and the computer device 1000 may include: processor 1001, network interface 1004, and memory 1005, in addition, the computer device 1000 may further comprise: a user interface 1003, and at least one communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may also include a standard wired interface, a wireless interface, among others. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 1005 may also optionally be at least one storage device located remotely from the aforementioned processor 1001. As shown in fig. 10, an operating system, a network communication module, a user interface module, and a device control application program may be included in the memory 1005, which is one type of computer-readable storage medium.

The network interface 1004 in the computer device 1000 may also provide network communication functions, and the optional user interface 1003 may also include a Display screen (Display) and a Keyboard (Keyboard). In the computer device 1000 shown in FIG. 10, the network interface 1004 may provide network communication functions; while user interface 1003 is primarily used as an interface for providing input to a user; and the processor 1001 may be used to invoke a device control application stored in the memory 1005 to implement:

It should be understood that the computer device 1000 described in the embodiment of the present application may perform the description of the data processing method in the embodiment corresponding to fig. 3 and fig. 8, and may also perform the description of the data processing apparatus 8 in the embodiment corresponding to fig. 3, which is not described herein. In addition, the description of the beneficial effects of the same method is omitted.

Furthermore, it should be noted here that: the embodiment of the present application further provides a computer readable storage medium, in which the computer program executed by the aforementioned data processing apparatus 8 is stored, and the computer program includes program instructions, when executed by a processor, can execute the description of the data processing method in the embodiment corresponding to fig. 3 and 8, and therefore, a description will not be repeated here. In addition, the description of the beneficial effects of the same method is omitted. For technical details not disclosed in the embodiments of the computer-readable storage medium according to the present application, please refer to the description of the method embodiments of the present application. As an example, program instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or, alternatively, across multiple computing devices distributed across multiple sites and interconnected by a communication network, where the multiple computing devices distributed across multiple sites and interconnected by the communication network may constitute a blockchain system.

In addition, it should be noted that: embodiments of the present application also provide a computer program product or computer program that may include computer instructions that may be stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor may execute the computer instructions, so that the computer device performs the description of the data processing method in the embodiment corresponding to fig. 3 and fig. 8, which will not be described in detail herein. In addition, the description of the beneficial effects of the same method is omitted. For technical details not disclosed in the computer program product or the computer program embodiments according to the present application, reference is made to the description of the method embodiments according to the present application.

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 order of action described, as some steps may be performed in other order or 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 for the present application.

The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.

The modules in the device of the embodiment of the application can be combined, divided and deleted according to actual needs.

In summary, by implementing the embodiment of the application, the object to be put is selected from the objects to be selected according to the object characteristics of the object to be selected and the object cluster characteristics corresponding to the feedback object with feedback on the target data. And acquiring the data set characteristics of the object to be put according to the operation data set of the object to be put. And then obtaining a matching value between the data set characteristic of the object to be put and the target data characteristic corresponding to the target data. And if the matching value is larger than the first threshold value, throwing target data into the object to be thrown. That is, the object to be put in which feedback operation is possibly performed on the target data is determined according to the object cluster characteristics acquired by the operation data of the target data, and then the object to be put in which the target data is put in the next time is determined according to the matching value between the data cluster characteristics acquired by the operation data of the object to be put and the data characteristics of the target data, so that data put in is realized in a closed link, the accuracy of the data put in the next time is improved, and the improvement of the put effect is facilitated.

Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored in a computer-readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.

The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.

Claims

1. A method of data processing, comprising:

responding to a scanning operation aiming at a unique identification code carried by target data, and displaying an information display page associated with the target data: the target data are displayed in a terminal screen of the public equipment;

acquiring object operation data and object attribute information associated with the information display page, and acquiring delivery object data and delivery scene data associated with the unique identification code;

Packaging the object operation data, the object attribute information, the delivery object data and the delivery scene data into data to be uplink with an association relationship, and performing uplink storage on the data to be uplink; the data to be uplink is used for determining the object to be launched of the target data in the subsequent launching scene.

2. The method of claim 1, wherein the encapsulating the object operation data, the object attribute information, the delivery object data, and the delivery scene data into to-be-uplinked data having an association relationship, and the storing the to-be-uplinked data includes:

setting association relations among the object operation data, the object attribute information, the delivery object data and the delivery scene data to obtain association data;

carrying out hash operation on the associated data to obtain hash data corresponding to the associated data, and packaging the hash data into data to be uplink;

writing the data to be uplinked into a data block, and broadcasting the data block in a block chain network so that a consensus node in the block chain network performs consensus operation on the data block;

If the data block consensus passes, the data block is added to the blockchain.

3. The method as recited in claim 1, further comprising:

acquiring an operation data set of the target data from a block chain, determining a plurality of feedback objects corresponding to the target data according to the operation data set of the target data, and acquiring object cluster characteristics of the plurality of feedback objects; the operation data set of the target data comprises the object operation data;

acquiring object characteristics of each object to be selected in an object set to be selected, and determining an object to be put in from the object set to be selected according to the object characteristics of each object to be selected and the object cluster characteristics;

acquiring an operation data set of the object to be put from the blockchain, and acquiring the data set characteristics of the object to be put according to the operation data set of the object to be put;

acquiring a matching value between the data set characteristic of the object to be put and the data characteristic corresponding to the target data;

and if the matching value is larger than a first threshold value, throwing the target data into the object to be thrown.

4. The method of claim 3, wherein the obtaining object cluster features of the plurality of feedback objects comprises:

Acquiring operation data of a feedback object a on first data, and acquiring a preference value of the feedback object a on the first data according to the operation data of the feedback object a on the first data; the first data comprises the target data, and the feedback object a belongs to the plurality of feedback objects;

acquiring target operation data from operation data of the feedback object a on the first data according to preference values of the feedback object a on the first data, and acquiring target object attributes corresponding to feedback time of the target operation data from an object attribute set of the feedback object a;

according to the object throwing data, the scene throwing data and the object attributes corresponding to the target operation data, obtaining the object preference characteristics of the feedback object a;

and acquiring the object weight of the feedback object a and the data weight of the first data, and acquiring the object cluster characteristics of the feedback objects according to the object weight and the object preference characteristics of the feedback object a and the data weight of the first data.

5. The method of claim 4, wherein the obtaining the object weight of the feedback object a and the data weight of the first data comprises:

Acquiring a first number of operation data sets of the feedback object a and a first value of the feedback object a; acquiring a second number of operational data sets of the first data and a second value of the first data;

acquiring the object weight of the feedback object a according to the first quantity and the first value; and acquiring the data weight of the first data according to the second quantity and the second value.

6. The method of any of claims 3-5, wherein the set of operational data of the object to be put comprises operational data of the object to be put on the second data;

the obtaining the data set characteristics of the object to be put according to the operation data set of the object to be put comprises the following steps:

acquiring a preference value of the object to be put on the second data according to the operation data of the object to be put on the second data;

acquiring data preference characteristics of the second data according to the throwing object data and throwing scene data of the second data and preference values of the objects to be thrown on the second data;

and acquiring the data weight of the second data and the object weight of the object to be put, and acquiring the data set characteristic of the object to be put according to the data weight and the data preference characteristic of the second data.

7. The method of claim 6, wherein the method further comprises:

receiving a uplink request of the object to be put on to the operation data of the second data;

acquiring the throwing value of the object to be thrown according to the object attribute set of the object to be thrown, historical operation data and throwing object data corresponding to the historical operation data;

and if the throwing value of the object to be thrown is larger than a second threshold value, performing uplink storage on the operation data of the object to be thrown aiming at the target data, and throwing object data and throwing scene data corresponding to the operation data.

8. The method of any one of claims 3-5, wherein the method further comprises:

acquiring a tag set and the number of coverage objects of the target data according to the attribute information of the target data, and acquiring tag sets respectively corresponding to a plurality of candidate objects;

if the tag set of the candidate object b in the plurality of candidate objects comprises the tag set of the target data, taking the candidate object b as a first object;

if the number of the first objects is smaller than the number of the covered objects, performing object expansion based on the tag set of the first objects to obtain second objects; the sum between the number of the first objects and the number of the second objects is equal to the number of the overlay objects;

And throwing the target data into the first object and the second object.

9. A data processing apparatus, comprising:

the data acquisition module is used for acquiring object operation data and object attribute information associated with the information display page and acquiring delivery object data and delivery scene data associated with the unique identification code;

the storage module is used for packaging the object operation data, the object attribute information, the put object data and the put scene data into to-be-uplink data with an association relationship, and storing the to-be-uplink data in a uplink manner; the data to be uplink is used for determining the object to be launched of the target data in the subsequent launching scene.

10. A computer device comprising a memory and a processor; the memory is connected to the processor for storing a computer program, the processor being adapted to invoke the computer program to cause the computer device to perform the method of any of claims 1-8.

11. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program adapted to be loaded and executed by a processor to cause a computer device having the processor to perform the method of any of claims 1-8.