CN113077286A

CN113077286A - Interest retrieval method based on block chain and artificial intelligence and big data mining center

Info

Publication number: CN113077286A
Application number: CN202110381334.8A
Authority: CN
Inventors: 陈炜炜
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-11-11
Filing date: 2020-11-11
Publication date: 2021-07-06
Also published as: CN113077285A; CN112308626A; CN112308626B

Abstract

The embodiment of the application provides an interest retrieval method and a big data mining center based on a block chain and artificial intelligence, user interest learning information of a plurality of advertisement drainage paths is determined based on an advertisement drainage skip subgraph and then is respectively input into a plurality of interest retrieval units in an interest retrieval model, at least one advertisement attention content matching is carried out to obtain at least one advertisement attention content, the at least one advertisement attention content matching is carried out based on a related big data retrieval subarea, the related big data retrieval subarea is related to advertisement attention contents extracted by other interest retrieval units of the plurality of interest retrieval units, therefore, at least one exchange and fusion can be carried out between the advertisement attention contents extracted from different interest retrieval units, and then the interest retrieval of the advertisement attention contents of different levels can be carried out, so that the representation capability of the crowd interest point retrieval is improved through the levels of enriching the advertisement attention contents, thereby the retrieval is more targeted.

Description

Interest retrieval method based on block chain and artificial intelligence and big data mining center

The application is a divisional application with the application date of 2020, 11/11, and the application number of 202011253976.1, and the name of the invention is 'advertisement push method and big data mining center based on block chain and artificial intelligence'.

Technical Field

The application relates to the technical field of advertisement pushing based on artificial intelligence, in particular to an interest retrieval method based on a block chain and artificial intelligence and a big data mining center.

Background

In people's daily life, various advertisements are ubiquitous, the advertisement market is turning from mass marketing to mass marketing, and in the era that products and consumers are subdivided continuously, the limitations of traditional media cannot effectively distinguish target audience groups of products.

For advertising manufacturers, the advertising strategy of the advertising manufacturers relates to a large amount of data optimization, and how to optimize the advertising strategy information in real time under the multi-advertising strategy scene, so that the accuracy of configuration information in the advertising strategy detection process is improved, and the subsequent advertising strategy processing effect is improved.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present application is to provide an interest retrieval method and a big data mining center based on a block chain and artificial intelligence, which generate advertisement push optimization information corresponding to each crowd interest point according to crowd interest point information corresponding to each crowd interest point by obtaining crowd interest point information corresponding to each crowd interest point in a preset number of crowd interest points, then process advertisement strategy start information corresponding to each crowd interest point by using the advertisement push optimization information corresponding to each crowd interest point to obtain advertisement strategy confirmation information corresponding to each crowd interest point, and finally generate an advertisement push control process for advertisement push through the block chain according to the advertisement strategy confirmation information corresponding to each crowd interest point. By adopting the mode, the advertisement strategy information from different dimensions is processed in parallel based on the plurality of crowd interest points, and then under the multi-advertisement strategy scene, the advertisement putting strategy node strengthening or the advertisement putting strategy node optimizing can be carried out on the advertisement strategy information on different dimensions through the advertisement pushing optimizing information, so that the advertisement strategy information can be optimized in real time, the accuracy of configuration information in the advertisement strategy detection process is improved, and the subsequent advertisement strategy processing effect is favorably improved.

In a first aspect, the present application provides an advertisement push method based on a block chain and artificial intelligence, which is applied to a big data mining center, where the big data mining center is in communication connection with a plurality of advertisement service terminals, and the method includes:

obtaining visitor interest data corresponding to advertisement push feedback information of a target advertisement push task, and searching crowd interest points based on the visitor interest data;

acquiring crowd interest point information corresponding to each crowd interest point in a preset number of crowd interest points, wherein the crowd interest point information comprises crowd interest point coverage labels, interest point advertisement labels and access attribute information, the crowd interest point coverage labels are used for the crowd labels covered by the crowd interest points, the interest point advertisement labels are used for indicating advertisement attention contents of the crowd interest points, and the access attribute information is used for indicating advertisement access conditions in the crowd interest points;

generating advertisement pushing optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, wherein the advertisement pushing optimization information is used for carrying out advertisement putting strategy node optimization processing or advertisement putting strategy node strengthening processing on advertisement strategy starting information, and the advertisement pushing optimization information and the crowd interest points have one-to-one correspondence relation;

and processing the advertisement strategy starting information corresponding to each crowd interest point by adopting the advertisement push optimization information corresponding to each crowd interest point to obtain the advertisement strategy confirmation information corresponding to each crowd interest point, and generating an advertisement push control process for pushing the advertisement through a block chain according to the advertisement strategy confirmation information corresponding to each crowd interest point, wherein the advertisement push optimization information, the advertisement strategy starting information and the advertisement strategy confirmation information have a one-to-one correspondence relationship.

In a possible implementation manner of the first aspect, the obtaining of the crowd interest point information corresponding to each crowd interest point in the preset number of crowd interest points includes:

detecting each crowd interest point in the preset number of crowd interest points to obtain an advertisement access detection result corresponding to each crowd interest point;

determining access attribute information corresponding to each crowd interest point according to the advertisement access detection result corresponding to each crowd interest point;

determining access migration relationship information corresponding to each crowd interest point according to the advertisement access detection result corresponding to each crowd interest point;

acquiring a crowd interest point coverage label corresponding to each crowd interest point and an interest point advertisement label corresponding to each crowd interest point;

and generating the crowd interest point information corresponding to each crowd interest point according to the access attribute information corresponding to each crowd interest point, the access migration relationship information corresponding to each crowd interest point, the crowd interest point coverage label corresponding to each crowd interest point and the interest point advertisement label corresponding to each crowd interest point.

In a possible implementation manner of the first aspect, the determining, according to the advertisement access detection result corresponding to each of the crowd interest points, access migration relationship information corresponding to each of the crowd interest points includes:

and aiming at any one crowd interest point in the preset number of crowd interest points, if the advertisement access detection result indicates that access object information with access node migration exists in the crowd interest points, determining the information of the access node migration process of the access object information in the crowd interest points as access migration relation information corresponding to each crowd interest point.

In a possible implementation manner of the first aspect, the generating, according to the crowd interest point information corresponding to each crowd interest point, advertisement push optimization information corresponding to each crowd interest point includes:

acquiring relation entity information corresponding to the interest point advertisement label from advertisement push strategy demand information corresponding to the pre-configured crowd interest point coverage label;

extracting the characteristics of the relationship entity information according to the access attribute information to obtain pushing optimization scene information of which the relationship entity information is respectively matched with the access attribute information;

determining global advertisement push optimization information based on push optimization scenario information of the relational entity information;

determining access migration relationship entity information in the relationship entity information according to the access migration relationship information, and determining push optimization scenario information corresponding to the access migration relationship entity information;

fusing the global advertisement push optimization information and push optimization scenario information corresponding to the access migration relationship entity information to obtain advertisement push optimization information corresponding to each crowd interest point, wherein the advertisement push optimization information comprises advertisement putting strategy nodes which need to perform advertisement putting strategy node optimization processing or advertisement putting strategy node enhancement processing on the advertisement strategy initial information;

the access attribute information comprises an advertisement access thermodynamic diagram, the advertisement access thermodynamic diagram comprises a plurality of thermal units and thermal keyword characteristics connected between the two thermal units, the thermal keyword characteristics comprise thermal keyword labels and thermal keyword word vectors of the thermal keyword characteristics, and the thermal units comprise relationship entity thermal nodes and a hot spot map;

the performing feature extraction on the relationship entity information according to the access attribute information to obtain push optimized context information in which the relationship entity information is respectively matched with the access attribute information includes:

determining a relationship entity thermodynamic node corresponding to the relationship entity information in the advertisement access thermodynamic diagram;

determining the thermal keyword parameter of the relationship entity thermal node and optimizing the thermal keyword parameter according to the thermal keyword label in a plurality of thermal units of the advertisement access thermodynamic diagram;

calculating a first pushing optimization scene generated by the released thermal keyword parameters on the relationship entity information according to a thermal keyword word vector of the thermal keyword characteristics connected between the relationship entity thermal node and the released thermal keyword parameters;

calculating a second pushing optimization scene generated by the released thermal keyword parameters on the relationship entity information according to the thermal keyword word vector of the thermal keyword characteristics connected between the relationship entity thermal nodes and the released thermal keyword parameters;

determining push optimization scenario information of the relationship entity information according to the first push optimization scenario and the second push optimization scenario;

the step of processing the advertisement strategy initiation information corresponding to each crowd interest point by using the advertisement push optimization information corresponding to each crowd interest point to obtain the advertisement strategy confirmation information corresponding to each crowd interest point comprises the following steps:

according to the advertisement delivery optimization information, the advertisement delivery strategy node which needs to carry out advertisement delivery strategy node optimization processing or advertisement delivery strategy node strengthening processing on the advertisement strategy initial information is included, a first advertisement delivery strategy node which needs to carry out advertisement delivery strategy node optimization processing on the advertisement strategy initial information and a second advertisement delivery strategy node which needs to carry out advertisement delivery strategy node strengthening processing on the advertisement strategy initial information are obtained;

and optimizing the first advertisement delivery strategy node according to the corresponding optimization strategy information in the advertisement delivery optimization information, and performing reinforcement processing on the second advertisement delivery strategy node according to the corresponding reinforcement strategy information in the advertisement delivery optimization information.

In a possible implementation manner of the first aspect, the generating an advertisement push control process for pushing an advertisement through a block chain according to the advertisement policy confirmation information corresponding to each of the crowd interest points includes:

determining an advertisement push preference intention matrix corresponding to each crowd interest point according to the advertisement strategy confirmation information corresponding to each crowd interest point, wherein the advertisement push preference intention matrix is an advertisement push preference intention matrix of the advertisement strategy confirmation information on each advertisement strategy unit;

determining the pushing control information of the advertisement pushing control link file corresponding to each crowd interest point according to the advertisement pushing preference intention matrix corresponding to each crowd interest point;

and carrying out redirection configuration on the current advertisement push control link file based on the push control information of the advertisement push control link file corresponding to each crowd interest point to obtain an advertisement push control process corresponding to each crowd interest point.

In a possible implementation manner of the first aspect, the performing redirection configuration on the current advertisement push control link file based on the push control information of the advertisement push control link file corresponding to each crowd point of interest to obtain the advertisement push control process corresponding to each crowd point of interest includes:

acquiring push control associated information of the push control information of the advertisement push control link file corresponding to each crowd interest point aiming at each link jump information in the current advertisement push control link file;

and carrying out redirection configuration on the current advertisement push control link file based on the push control associated information of each link jump information in the current advertisement push control link file to obtain an advertisement push control process corresponding to each crowd interest point.

In a possible implementation manner of the first aspect, the step of obtaining visitor interest data corresponding to advertisement push feedback information of a target advertisement push task and performing crowd interest point retrieval based on the visitor interest data includes:

acquiring advertisement control node information corresponding to each advertisement push segment of the advertisement service terminal, and performing advertisement drainage search on advertisement push feedback information of a target advertisement push task based on the advertisement control node information to acquire a corresponding advertisement drainage search segment;

acquiring corresponding advertisement drainage jumping subgraphs based on the advertisement drainage searching fragments, and determining user interest learning information of a plurality of advertisement drainage paths based on the advertisement drainage jumping subgraphs;

respectively inputting the user interest learning information into a plurality of interest point retrieval units in an interest point retrieval model, and performing at least one time of advertisement attention content matching through the interest point retrieval units to obtain at least one advertisement attention content; the method comprises the steps that at least one time of matching of advertisement attention content through an interest point retrieval unit is carried out on the basis of an associated big data retrieval partition, the associated big data retrieval partition is associated with the advertisement attention content extracted by other interest point retrieval units of a plurality of interest point retrieval units, wherein the interest point retrieval unit is obtained on the basis of artificial intelligence network and corresponding training samples, and the training samples comprise user interest learning information samples and corresponding advertisement attention content labels;

and searching interest points of a plurality of advertisement attention contents output by the interest point searching units to obtain interest point searching contents, obtaining visitor interest data of the advertisement diversion skip subgraph under the advertisement push feedback information based on the interest point searching contents, and searching crowd interest points based on the visitor interest data of the advertisement diversion skip subgraph under the advertisement push feedback information.

In a possible implementation manner of the first aspect, the method further includes:

taking one interest point retrieval unit of the interest point retrieval units as a target interest point retrieval unit;

acquiring first advertisement attention content extracted by the target interest point retrieval unit and second advertisement attention content extracted by other interest point retrieval units except the target interest point retrieval unit in the plurality of interest point retrieval units;

when the advertisement drainage path of the second advertisement attention content does not match the advertisement drainage path of the first advertisement attention content, performing interest degree distribution marking on the second advertisement attention content, wherein the advertisement drainage path of the second advertisement attention content after interest degree distribution marking is the same as the advertisement drainage path of the first advertisement attention content;

matching the second advertisement attention content with the content matching information of the first advertisement attention content after the interest degree distribution is marked through the target interest point retrieval unit;

wherein the number of the second advertisement attention content is at least two; the method further comprises the following steps:

when second advertisement attention content of an advertisement drainage path, of which the advertisement drainage path does not match the first advertisement attention content, and second advertisement attention content of an advertisement drainage path, of which the advertisement drainage path matches the first advertisement attention content, exist at the same time, interest degree distribution marking is performed on the second advertisement attention content of the advertisement drainage path, of which the advertisement drainage path does not match the first advertisement attention content, reverse interest degree distribution marking is performed on the second advertisement attention content of which the advertisement drainage path matches the advertisement drainage path of the first advertisement attention content, and the advertisement drainage path of the second advertisement attention content after interest degree distribution marking and the advertisement drainage path of the second advertisement attention content after reverse interest degree distribution marking are the same as the advertisement drainage path of the first advertisement attention content;

performing advertisement attention content matching on the second advertisement attention content marked by the interest degree distribution, the second advertisement attention content marked by the reverse interest degree distribution and the content matching information of the first advertisement attention content through the target interest point retrieval unit;

wherein the method further comprises:

when the advertisement drainage path of the second advertisement attention content is matched with the advertisement drainage path of the first advertisement attention content, carrying out reverse interestingness distribution marking on the second advertisement attention content, wherein the advertisement drainage path of the second advertisement attention content after the reverse interestingness distribution marking is the same as the advertisement drainage path of the first advertisement attention content;

and matching the second advertisement attention content with the content matching information of the first advertisement attention content after the reverse interestingness distribution is marked by the target interest point retrieval unit.

In a possible implementation manner of the first aspect, the user interest learning information at least includes first user interest learning information, second user interest learning information, and third user interest learning information, the interest point retrieval model includes a first interest point retrieval unit, a second interest point retrieval unit, and a third interest point retrieval unit, where the first user interest learning information, the second user interest learning information, and the third user interest learning information respectively correspond to user interest learning information of an interest coverage learning segment, an interest data source segment, and an interest output source segment, and the first interest point retrieval unit, the second interest point retrieval unit, and the third interest point retrieval unit respectively correspond to interest point retrieval units of the interest coverage learning segment, the interest data source segment, and the interest output source segment;

the step of inputting the user interest learning information into a plurality of interest point retrieval units in an interest point retrieval model respectively, and performing at least one time of advertisement attention content matching through the interest point retrieval units to obtain at least one advertisement attention content comprises the following steps:

inputting the first user interest learning information into a first interest point retrieval unit to perform advertisement attention content matching of a first interest point retrieval layer, and obtaining advertisement attention content extracted by the first interest point retrieval unit on the first interest point retrieval layer;

performing interest point retrieval on the advertisement attention content extracted by the first interest point retrieval unit on a first interest point retrieval layer and the second user interest learning information to obtain corresponding interest point retrieval elements of the second interest point retrieval unit on a second interest point retrieval layer;

acquiring advertisement attention content extracted by the first interest point retrieval unit on a first interest point retrieval layer, and using the advertisement attention content as an interest point retrieval element corresponding to the first interest point retrieval unit on a second interest point retrieval layer;

performing first advertisement attention content matching of a second interest point retrieval layer on the corresponding interest point retrieval elements of the first interest point retrieval unit in the second interest point retrieval layer through the first interest point retrieval unit to obtain first extracted advertisement attention content of the first interest point retrieval unit in the second interest point retrieval layer;

performing first advertisement attention content matching of a second interest point retrieval layer on the corresponding interest point retrieval elements of the second interest point retrieval unit in the second interest point retrieval layer through the second interest point retrieval unit to obtain first extracted advertisement attention content of the second interest point retrieval unit in the second interest point retrieval layer;

the advertisement attention content firstly extracted by the first interest point retrieval unit at a second interest point retrieval layer is transmitted to the second interest point retrieval unit, and the advertisement attention content firstly extracted by the second interest point retrieval unit at the second interest point retrieval layer is transmitted to the first interest point retrieval unit;

the first interest point retrieval unit is used for carrying out interest point retrieval on the advertisement attention content firstly extracted by the first interest point retrieval unit in a second interest point retrieval layer and the advertisement attention content transmitted by the second interest point retrieval unit, and carrying out advertisement attention content matching on content matching information;

the second interest point retrieval unit is used for carrying out interest point retrieval on the advertisement attention content firstly extracted by the second interest point retrieval unit in a second interest point retrieval layer and the advertisement attention content transmitted by the first interest point retrieval unit, and carrying out advertisement attention content matching on content matching information;

performing interest point retrieval on the advertisement attention content extracted by the first interest point retrieval unit on a second interest point retrieval layer, the advertisement attention content extracted by the second interest point retrieval unit on the second interest point retrieval layer and the third user interest learning information to obtain an interest point retrieval element corresponding to the third interest point retrieval unit on a third interest point retrieval layer;

the first interest point retrieval unit is used for matching the advertisement attention content of a third interest point retrieval layer based on the advertisement attention content extracted by the first interest point retrieval unit on the second interest point retrieval layer, the second interest point retrieval unit is used for matching the advertisement attention content of the third interest point retrieval layer based on the advertisement attention content extracted by the second interest point retrieval unit on the second interest point retrieval layer, and the third interest point retrieval unit is used for matching the advertisement attention content of the third interest point retrieval layer with the corresponding interest point retrieval element of the third interest point retrieval unit on the third interest point retrieval layer.

In a second aspect, an embodiment of the present application further provides an advertisement pushing device based on a block chain and artificial intelligence, which is applied to a big data mining center, where the big data mining center is in communication connection with a plurality of advertisement service terminals, and the device includes:

the first acquisition module is used for acquiring visitor interest data corresponding to advertisement push feedback information of a target advertisement push task and retrieving crowd interest points based on the visitor interest data;

the second acquisition module is used for acquiring crowd interest point information corresponding to each crowd interest point in a preset number of the crowd interest points, wherein the crowd interest point information comprises crowd interest point coverage labels, interest point advertisement labels and access attribute information, the crowd interest point coverage labels are used for the crowd labels covered by the crowd interest points, the interest point advertisement labels are used for indicating advertisement attention contents of the crowd interest points, and the access attribute information is used for indicating advertisement access conditions in the crowd interest points;

a first generation module, configured to generate advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, where the advertisement push optimization information is used to perform advertisement delivery policy node optimization processing or advertisement delivery policy node enhancement processing on advertisement policy start information, and the advertisement push optimization information and the crowd interest points have a one-to-one correspondence relationship;

and the second generation module is used for processing the advertisement strategy starting information corresponding to each crowd interest point by adopting the advertisement push optimization information corresponding to each crowd interest point to obtain the advertisement strategy confirmation information corresponding to each crowd interest point, and generating an advertisement push control process for pushing the advertisement through a block chain according to the advertisement strategy confirmation information corresponding to each crowd interest point, wherein the advertisement push optimization information, the advertisement strategy starting information and the advertisement strategy confirmation information have a one-to-one correspondence relationship.

In a third aspect, an embodiment of the present application further provides an advertisement push system based on a block chain and artificial intelligence, where the advertisement push system based on the block chain and artificial intelligence includes a big data mining center and a plurality of advertisement service terminals communicatively connected to the big data mining center;

the big data mining center is used for:

In a fourth aspect, an embodiment of the present application further provides a big data mining center, where the big data mining center includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one advertisement service terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the block chain and artificial intelligence based advertisement pushing method in the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer executes the method for pushing an advertisement based on a blockchain and artificial intelligence in the first aspect or any one of the possible implementations of the first aspect.

Based on any one of the above aspects, the method includes obtaining crowd interest point information corresponding to each crowd interest point in a preset number of crowd interest points, generating advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, processing advertisement strategy starting information corresponding to each crowd interest point by adopting the advertisement push optimization information corresponding to each crowd interest point, obtaining advertisement strategy confirmation information corresponding to each crowd interest point, and finally generating an advertisement push control process for pushing advertisements through a block chain according to the advertisement strategy confirmation information corresponding to each crowd interest point. By adopting the mode, the advertisement strategy information from different dimensions is processed in parallel based on the plurality of crowd interest points, and then under the multi-advertisement strategy scene, the advertisement putting strategy node strengthening or the advertisement putting strategy node optimizing can be carried out on the advertisement strategy information on different dimensions through the advertisement pushing optimizing information, so that the advertisement strategy information can be optimized in real time, the accuracy of configuration information in the advertisement strategy detection process is improved, and the subsequent advertisement strategy processing effect is favorably improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic application scenario diagram of an advertisement push system based on a blockchain and artificial intelligence provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of an advertisement push method based on a blockchain and artificial intelligence according to an embodiment of the present disclosure;

fig. 3 is a schematic functional module diagram of an advertisement push device based on a blockchain and artificial intelligence provided in an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a big data mining center for implementing the above advertisement push method based on a blockchain and artificial intelligence according to an embodiment of the present disclosure.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interaction diagram of an advertisement push system 10 based on a blockchain and artificial intelligence according to an embodiment of the present application. The advertisement push system 10 based on the blockchain and artificial intelligence can comprise a big data mining center 100 and an advertisement service terminal 200 which is in communication connection with the big data mining center 100. The blockchain and artificial intelligence based advertisement delivery system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the blockchain and artificial intelligence based advertisement delivery system 10 may also include only a portion of the components shown in fig. 1 or may also include other components.

In this embodiment, the big data mining center 100 and the advertisement service terminal 200 in the advertisement push system 10 based on the blockchain and the artificial intelligence may cooperatively execute the advertisement push method based on the blockchain and the artificial intelligence described in the following method embodiment, and the detailed description of the following method embodiment may be referred to in the execution step portions of the big data mining center 100 and the advertisement service terminal 200.

To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of an advertisement push method based on a blockchain and artificial intelligence provided in an embodiment of the present application, where the advertisement push method based on a blockchain and artificial intelligence provided in this embodiment may be executed by the big data mining center 100 shown in fig. 1, and the advertisement push method based on a blockchain and artificial intelligence is described in detail below.

Step S110, visitor interest data corresponding to the advertisement push feedback information of the target advertisement push task is obtained, and crowd interest point retrieval is carried out based on the visitor interest data.

Step S120, obtaining the crowd interest point information corresponding to each crowd interest point in the preset number of crowd interest points.

In this embodiment, the crowd interest point information may specifically include a crowd interest point coverage tag, an interest point advertisement tag, and access attribute information. For example, the crowd interest point coverage tag may be used for a crowd tag covered by a crowd interest point (for example, a crowd tag using a certain APP, a crowd tag scanning a certain product two-dimensional code, or the like), the interest point advertisement tag is used for indicating advertisement attention content of the crowd interest point, the advertisement attention content may be a user click content area corresponding to an advertisement playing stream, and the access attribute information is used for indicating an advertisement access condition in the crowd interest point, for example, a click access condition, a collection access condition, a sharing access condition, or the like.

Step S130, generating advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point.

In this embodiment, the advertisement push optimization information is used to perform advertisement delivery policy node optimization processing or advertisement delivery policy node enhancement processing on the advertisement policy initiation information, and the advertisement push optimization information and the crowd interest points have a one-to-one correspondence relationship. The advertisement putting strategy node optimization processing on the advertisement strategy initial information can mean that the advertisement strategy initial information has advertisement putting strategy nodes and needs adaptive optimization, so that the advertisement strategy initial information is adaptive to the crowd interest point information corresponding to the current crowd interest point. The advertisement putting strategy node strengthening processing on the advertisement strategy initial information can mean that the advertisement strategy initial information has the advertisement putting strategy node which needs to be strengthened in a self-adaptive mode, and the putting strength of the advertisement putting strategy node can be strengthened.

Step S140, adopting the advertisement push optimization information corresponding to each crowd interest point, processing the advertisement strategy starting information corresponding to each crowd interest point to obtain the advertisement strategy confirmation information corresponding to each crowd interest point, and generating an advertisement push control process for pushing the advertisement through the block chain according to the advertisement strategy confirmation information corresponding to each crowd interest point.

The advertisement pushing optimization information, the advertisement strategy starting information and the advertisement strategy confirmation information have a one-to-one correspondence relationship. In this way, by generating the final advertisement push control process, the configuration information for performing crowd interest point retrieval on each advertisement attention content can be regenerated based on the final advertisement push control process, thereby performing closed-loop feedback control.

Based on the above steps, the present embodiment can generate advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, and then process the advertisement policy start information corresponding to each crowd interest point to obtain the advertisement policy confirmation information corresponding to each crowd interest point, thereby generating an advertisement push control process for pushing advertisements through a block chain. By adopting the mode, the advertisement strategy information from different dimensions is processed in parallel based on the plurality of crowd interest points, and then under the multi-advertisement strategy scene, the advertisement putting strategy node strengthening or the advertisement putting strategy node optimizing can be carried out on the advertisement strategy information on different dimensions through the advertisement pushing optimizing information, so that the advertisement strategy information can be optimized in real time, the accuracy of configuration information in the advertisement strategy detection process is improved, and the subsequent advertisement strategy processing effect is favorably improved.

In a possible implementation manner, for step S120, in the process of obtaining the crowd point-of-interest information corresponding to each crowd point-of-interest in the preset number of crowd points-of-interest, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S121, detecting each crowd interest point in a preset number of crowd interest points to obtain an advertisement access detection result corresponding to each crowd interest point.

And a substep S122, determining access attribute information corresponding to each crowd interest point according to the advertisement access detection result corresponding to each crowd interest point.

And a substep S123 of determining access migration relationship information corresponding to each crowd interest point according to the advertisement access detection result corresponding to each crowd interest point.

For example, for any one of a preset number of crowd interest points, if the advertisement access detection result indicates that access object information with access node migration exists in the crowd interest points, the information of the access node migration process of the access object information in the crowd interest points is determined as access migration relationship information corresponding to each crowd interest point.

And a substep S124 of obtaining a crowd interest point coverage label corresponding to each crowd interest point and an interest point advertisement label corresponding to each crowd interest point.

And a substep S125, generating crowd interest point information corresponding to each crowd interest point according to the access attribute information corresponding to each crowd interest point, the access migration relationship information corresponding to each crowd interest point, the crowd interest point coverage label corresponding to each crowd interest point and the interest point advertisement label corresponding to each crowd interest point.

Further, on this basis, as a possible implementation manner, in the process of generating the advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, aiming at the step S130, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S131 of obtaining the relationship entity information corresponding to the interest point advertisement tag from the advertisement push strategy demand information corresponding to the pre-configured crowd interest point overlay tag.

And a substep S132, extracting the characteristics of the relationship entity information according to the access attribute information to obtain the push optimized scene information of which the relationship entity information is respectively matched with the access attribute information.

And a substep S133 of determining global advertisement push optimization information based on the push optimization scenario information of the relationship entity information.

And a substep S134, determining the access migration relationship entity information in the relationship entity information according to the access migration relationship information, and determining the push optimization scenario information corresponding to the access migration relationship entity information.

And a substep S135, fusing the global advertisement push optimization information and the push optimization scenario information corresponding to the access migration relationship entity information to obtain advertisement push optimization information corresponding to each crowd interest point.

In this embodiment, the advertisement push optimization information includes an advertisement delivery policy node that needs to perform advertisement delivery policy node optimization processing or advertisement delivery policy node enhancement processing on the advertisement policy initiation information.

The access attribute information comprises an advertisement access thermodynamic diagram, the advertisement access thermodynamic diagram comprises a plurality of thermal units and thermal keyword characteristics connected between the two thermal units, the thermal keyword characteristics comprise thermal keyword labels and thermal keyword word vectors of the thermal keyword characteristics, and the thermal units comprise relationship entity thermal nodes and a hot spot map.

On this basis, for the sub-step S132, it can be realized by the following exemplary embodiments.

(1) And determining a relationship entity thermodynamic node corresponding to the relationship entity information in the advertisement access thermodynamic diagram.

(2) And determining the thermal keyword parameter of the relationship entity thermal node and optimizing the thermal keyword parameter in a plurality of thermal units of the advertisement access thermodynamic diagram according to the thermal keyword label.

(3) And calculating a first push optimization scene generated by the released thermal keyword parameters to the relationship entity information according to the thermal keyword word vectors of the thermal keyword characteristics between the connection relationship entity thermal nodes and the released thermal keyword parameters.

(4) And calculating a second pushing optimization scene generated by the released thermal keyword parameters to the relationship entity information according to the thermal keyword word vector of the thermal keyword characteristics between the connection relationship entity thermal nodes and the released thermal keyword parameters.

(5) And determining the push optimization scenario information of the relationship entity information according to the first push optimization scenario and the second push optimization scenario.

Thus, for step S140, in the process of obtaining the advertisement policy confirmation information corresponding to each crowd point of interest by processing the advertisement policy start information corresponding to each crowd point of interest by using the advertisement push optimization information corresponding to each crowd point of interest, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S141, obtaining a first advertisement putting strategy node for performing the advertisement putting strategy node optimization processing on the advertisement strategy initial information and a second advertisement putting strategy node for performing the advertisement putting strategy node enhancement processing on the advertisement strategy initial information according to the advertisement putting strategy node which comprises the advertisement putting strategy node optimization processing or the advertisement putting strategy node enhancement processing on the advertisement strategy initial information.

And a substep S142, optimizing the first advertisement delivery strategy node according to the corresponding optimization strategy information in the advertisement delivery optimization information, and performing reinforcement processing on the second advertisement delivery strategy node according to the corresponding reinforcement strategy information in the advertisement delivery optimization information.

In a possible implementation manner, still regarding step S140, in the process of generating an advertisement push control process for pushing advertisements through a blockchain according to the advertisement policy confirmation information corresponding to each crowd interest point, the process may be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S143, determining an advertisement push preference intention matrix corresponding to each crowd interest point according to the advertisement strategy confirmation information corresponding to each crowd interest point, wherein the advertisement push preference intention matrix is the advertisement push preference intention matrix of the advertisement strategy confirmation information on each advertisement strategy unit.

And a substep S144, determining the pushing control information of the advertisement pushing control link file corresponding to each crowd interest point according to the advertisement pushing preference intention matrix corresponding to each crowd interest point.

And a substep S145, performing redirection configuration on the current advertisement push control link file based on the push control information of the advertisement push control link file corresponding to each crowd interest point to obtain an advertisement push control process corresponding to each crowd interest point.

For example, the push control associated information of the push control information of the advertisement push control link file corresponding to each crowd interest point for each link jump information in the current advertisement push control link file may be obtained, and the current advertisement push control link file may be redirected and configured based on the push control associated information of each link jump information in the current advertisement push control link file, so as to obtain the advertisement push control process corresponding to each crowd interest point.

In a possible implementation manner, based on the above description, for step S110, in the process of obtaining visitor interest data corresponding to the advertisement push feedback information of the target advertisement push task and performing the search for the crowd interest point based on the visitor interest data, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S111 of obtaining advertisement control node information corresponding to each advertisement push segment of the advertisement service terminal, and performing advertisement drainage search on advertisement push feedback information of the target advertisement push task based on the advertisement control node information to obtain a corresponding advertisement drainage search segment.

In this embodiment, the advertisement control node information may be used to represent a control node instruction for performing advertisement drainage search interest point retrieval during the playing process of the advertisement playing stream, the target advertisement pushing task may refer to a pushing control service item of the advertisement pushing task data, and the advertisement pushing feedback information may refer to a production monitoring configuration parameter related to the initiated production monitoring request. The advertisement drainage search segment may be a search segment that is obtained from a preconfigured index database for the advertisement push feedback information of the target advertisement push task and matches with the control node instruction retrieved by each advertisement drainage search interest point, and specifically may include drainage behavior obtained by drainage, drainage duration, and the like.

And a substep S112, acquiring corresponding advertisement drainage jumping subgraphs based on the advertisement drainage searching segments, and determining user interest learning information of a plurality of advertisement drainage paths based on the advertisement drainage jumping subgraphs.

In this embodiment, the advertisement diversion skip subgraph may include skip record conditions of advertisement diversion processes in each advertisement playing process, the advertisement diversion path may refer to an advertisement diversion path node set formed by each advertisement playing process, and the user interest learning information may refer to a feature extraction condition of user interest in each advertisement playing process in scheduling the advertisement diversion process.

And a substep S113, respectively inputting the interest learning information of the user into a plurality of interest point retrieval units in the interest point retrieval model, and performing at least one time of advertisement attention content matching through the interest point retrieval units to obtain at least one advertisement attention content.

In this embodiment, at least one matching of the advertisement attention content by the interest point retrieving unit is performed based on the associated big data retrieving partition, and the associated big data retrieving partition is associated with the advertisement attention content extracted by other interest point retrieving units of the plurality of interest point retrieving units. The interest point retrieval unit is obtained based on an artificial intelligence network and corresponding training samples in a training mode, and the training samples comprise user interest learning information samples and corresponding advertisement attention content labels.

And a substep S114, performing interest point retrieval on the plurality of advertisement attention contents output by the plurality of interest point retrieval units to obtain interest point retrieval contents, obtaining visitor interest data of the advertisement diversion skip subgraph under the advertisement push feedback information based on the interest point retrieval contents, and performing crowd interest point retrieval based on the visitor interest data of the advertisement diversion skip subgraph under the advertisement push feedback information.

In this embodiment, the visitor interest data of the advertisement drainage skip subgraph under the advertisement push feedback information may be used to represent a retrieval instruction running set of the advertisement drainage skip subgraph in a subsequent crowd interest point retrieval process, that is, a retrieval control instruction for controlling a flow direction relationship of retrieval data nodes in the crowd interest point retrieval process, so as to execute the computer program according to an execution sequence of the retrieval control instructions, thereby performing crowd interest point retrieval.

Based on the above steps, in this embodiment, the user interest learning information of a plurality of advertisement drainage paths is determined based on the advertisement drainage skip subgraph, the user interest learning information is respectively input into a plurality of interest point retrieval units in the interest point retrieval model, each interest point retrieval unit performs at least one advertisement attention content matching to obtain at least one advertisement attention content, and the at least one advertisement attention content matching is performed based on the associated big data retrieval partition, the associated big data retrieval partition is associated with the advertisement attention content extracted by other interest point retrieval units of the plurality of interest point retrieval units, wherein the interest point retrieval unit is obtained based on artificial intelligence network and corresponding training sample training, the training sample includes user interest learning information samples and corresponding advertisement attention content labels, so that the advertisement attention content extracted from different interest point retrieval units can be exchanged and fused at least once, and then, interest point retrieval can be carried out on advertisement attention contents in different levels, so that the representation capability of crowd interest point retrieval is improved by enriching the levels of the advertisement attention contents, and the retrieval pertinence is better.

In a possible implementation manner, on the basis of the above scheme, in this embodiment, one of the interest point retrieving units may further serve as a target interest point retrieving unit, and then a first advertisement attention content extracted by the target interest point retrieving unit and a second advertisement attention content extracted by other interest point retrieving units except the target interest point retrieving unit in the plurality of interest point retrieving units are obtained.

Therefore, when the advertisement drainage path of the second advertisement attention content is not matched with the advertisement drainage path of the first advertisement attention content, the interest degree distribution marking is carried out on the second advertisement attention content, and the advertisement drainage path of the second advertisement attention content after the interest degree distribution marking is the same as the advertisement drainage path of the first advertisement attention content. Therefore, the target interest point retrieval unit can be used for matching the second advertisement attention content marked by the interest degree distribution with the content matching information of the first advertisement attention content.

Wherein the number of the second advertisement attention content is at least two.

On the basis, when second advertisement attention content of the advertisement drainage path, of which the advertisement drainage path does not match the first advertisement attention content, and second advertisement attention content of the advertisement drainage path, of which the advertisement drainage path matches the first advertisement attention content exist at the same time, interest degree distribution marking is performed on the second advertisement attention content of the advertisement drainage path, of which the advertisement drainage path does not match the first advertisement attention content, reverse interest degree distribution marking is performed on the second advertisement attention content of the advertisement drainage path, of which the advertisement drainage path matches the first advertisement attention content, and the advertisement drainage path of the second advertisement attention content after interest degree distribution marking and the advertisement drainage path of the second advertisement attention content after reverse interest degree distribution marking are the same as the advertisement drainage path of the first advertisement attention content.

In this way, the target interest point retrieval unit may perform advertisement attention content matching on the second advertisement attention content marked by the interest degree distribution, the second advertisement attention content marked by the reverse interest degree distribution, and the content matching information of the first advertisement attention content.

For another example, on the basis, when the advertisement drainage path of the second advertisement attention content matches the advertisement drainage path of the first advertisement attention content, the reverse interestingness distribution marking is performed on the second advertisement attention content, and the advertisement drainage path of the second advertisement attention content after the reverse interestingness distribution marking is the same as the advertisement drainage path of the first advertisement attention content. In this way, the target interest point retrieval unit can be used for matching the second advertisement attention content marked by the reverse interestingness distribution with the content matching information of the first advertisement attention content.

For example, in one possible implementation manner, regarding step S113, in the process of inputting the user interest learning information into a plurality of interest point retrieving units in the interest point retrieving model respectively, and performing at least one advertisement attention content matching through the interest point retrieving units to obtain at least one advertisement attention content, the following exemplary sub-steps can be implemented, which are described in detail below.

In the substep S1131, the user interest learning information is input to a plurality of interest point retrieval units in the interest point retrieval model, respectively.

And a substep S1132, for one of the interest point retrieval units, performing advertisement attention content matching on corresponding user interest learning information through the interest point retrieval unit, acquiring second advertisement attention content extracted through other interest point retrieval units of the interest point retrieval units and performing partition service matching on the first advertisement attention content after the first advertisement attention content is obtained through the advertisement attention content matching, and continuing to perform advertisement attention content matching based on a partition service matching result so as to perform advertisement attention content matching and partition service matching alternately.

Still further, in step S112, in the process of determining the user interest learning information of a plurality of advertisement drainage paths based on the advertisement drainage skip subgraph, index matching of the advertisement drainage paths may be performed on the advertisement drainage skip subgraph to obtain the user interest learning information of a plurality of different advertisement drainage paths.

For example, the retrieval information nodes of the advertisement diversion skip subgraph for each advertisement diversion path can be indexed and matched, and all the retrieval information nodes are spliced according to the association relationship among the retrieval information nodes, so that the user interest learning information of a plurality of different advertisement diversion paths is obtained.

Thus, in another parallel possible implementation manner, for step S113, in the process of inputting the user interest learning information into a plurality of interest point retrieving units in the interest point retrieving model respectively, and performing at least one advertisement attention content matching through the interest point retrieving units to obtain at least one advertisement attention content, the following exemplary sub-steps can be implemented, which are described in detail below.

In the substep S1133, the user interest learning information is input to a plurality of interest point retrieval units in the interest point retrieval model, respectively.

The user interest learning information is respectively in one-to-one correspondence with one of the interest point retrieval units.

And a substep S1134, performing at least one advertisement attention content matching on the corresponding user interest learning information through the interest point retrieval unit. The extracted advertisement drainage path of the advertisement attention content is consistent with the advertisement drainage path of the user interest learning information corresponding to the interest point retrieval unit.

In a parallel possible implementation manner, the user interest learning information at least includes first user interest learning information, second user interest learning information, and third user interest learning information, the interest point retrieval model includes a first interest point retrieval unit, a second interest point retrieval unit, and a third interest point retrieval unit, where the first user interest learning information, the second user interest learning information, and the third user interest learning information respectively correspond to user interest learning information of an interest coverage learning segment, an interest data source segment, and an interest output source segment, and the first interest point retrieval unit, the second interest point retrieval unit, and the third interest point retrieval unit respectively correspond to interest point retrieval units of the interest coverage learning segment, the interest data source segment, and the interest output source segment.

On this basis, still referring to step S113, in the process of inputting the user interest learning information into the multiple interest point retrieving units in the interest point retrieving model respectively, and performing at least one advertisement attention content matching through the interest point retrieving units to obtain at least one advertisement attention content, the following exemplary sub-steps can be implemented, which are described in detail below.

And the substep S1135, inputting the first user interest learning information into the first interest point retrieval unit to match the advertisement attention content of the first interest point retrieval layer, so as to obtain the advertisement attention content extracted by the first interest point retrieval unit on the first interest point retrieval layer.

And the substep S1136 is to perform interest point retrieval on the advertisement attention content and the second user interest learning information extracted by the first interest point retrieval unit on the first interest point retrieval layer to obtain the corresponding interest point retrieval element of the second interest point retrieval unit on the second interest point retrieval layer.

In the sub-step S1137, the advertisement attention content extracted by the first interest point retrieving unit in the first interest point retrieving layer is obtained and used as the corresponding interest point retrieving element of the first interest point retrieving unit in the second interest point retrieving layer.

And the substep S1138, performing first advertisement attention content matching of the second interest point retrieval layer on the corresponding interest point retrieval elements of the second interest point retrieval layer of the first interest point retrieval unit through the first interest point retrieval unit to obtain the advertisement attention content firstly extracted by the first interest point retrieval unit in the second interest point retrieval layer.

And a substep S11391, performing first advertisement attention content matching on the interest point retrieval elements corresponding to the second interest point retrieval layer in the second interest point retrieval layer by the second interest point retrieval unit to obtain the advertisement attention content first extracted by the second interest point retrieval unit in the second interest point retrieval layer.

In the sub-step S11392, the advertisement attention content first extracted by the first interest point retrieving unit in the second interest point retrieving layer is transferred to the second interest point retrieving unit, and the advertisement attention content first extracted by the second interest point retrieving unit in the second interest point retrieving layer is transferred to the first interest point retrieving unit.

And a substep S11393, performing the interest point retrieval on the advertisement attention content firstly extracted by the first interest point retrieval unit in the second interest point retrieval layer and the advertisement attention content transmitted by the second interest point retrieval unit through the first interest point retrieval unit, and performing the advertisement attention content matching on the content matching information.

In the sub-step S11394, the second interest point retrieving unit performs the interest point retrieval on the advertisement attention content first extracted by the second interest point retrieving unit in the second interest point retrieving layer and the advertisement attention content transmitted by the first interest point retrieving unit, and performs the advertisement attention content matching on the content matching information.

And the substep S11395, performing interest point retrieval on the advertisement attention content extracted by the first interest point retrieval unit in the second interest point retrieval layer, the advertisement attention content extracted by the second interest point retrieval unit in the second interest point retrieval layer and the third user interest learning information to obtain the corresponding interest point retrieval element of the third interest point retrieval unit in the third interest point retrieval layer.

In the sub-step S11396, the first interest point retrieving unit performs advertisement attention content matching of the third interest point retrieving layer based on the advertisement attention content extracted by the first interest point retrieving unit in the second interest point retrieving layer, the second interest point retrieving unit performs advertisement attention content matching of the third interest point retrieving layer based on the advertisement attention content extracted by the second interest point retrieving unit in the second interest point retrieving layer, and the third interest point retrieving unit performs advertisement attention content matching of the third interest point retrieving layer on the interest point retrieving element corresponding to the third interest point retrieving unit in the third interest point retrieving layer.

Further, in a possible implementation manner, for step S114, in the process of retrieving content based on the interest point and obtaining visitor interest data of the advertisement drainage skip subgraph under the advertisement push feedback information, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S1141 of determining a retrieval model corresponding to the advertisement push feedback information.

And a substep S1142 of inputting the interest point retrieval content into a retrieval model and obtaining visitor interest data of the advertisement drainage skip subgraph under the advertisement push feedback information through the retrieval model.

The interest point retrieval model and the configuration mode of the retrieval model can be realized by the following exemplary embodiments:

(1) and obtaining the interest point retrieval content sample, the interest point retrieval model and the retrieval model, wherein the retrieval label of the interest point retrieval content sample is used for representing the labeling result of the interest point retrieval content sample under the advertisement push feedback information.

(2) And retrieving content samples based on the interest points, and determining user interest learning information samples of a plurality of advertisement drainage paths.

(3) And respectively inputting the user interest learning information samples into a plurality of interest point retrieval units in the interest point retrieval model, and performing at least one time of advertisement attention content matching through the interest point retrieval units to obtain at least one interest point retrieval content.

The method comprises the steps that at least one time of advertisement attention content matching through an interest point retrieval unit is carried out on the basis of an associated big data retrieval partition, and the associated big data retrieval partition is associated with interest point retrieval contents extracted by other interest point retrieval units of a plurality of interest point retrieval units.

(4) And performing interest point retrieval on the advertisement attention contents output by the interest point retrieval units to obtain target interest point retrieval contents.

(5) And inputting the target interest point retrieval content into the retrieval model, and obtaining an interest point retrieval result of the interest point retrieval content sample under the advertisement push feedback information through the retrieval model.

(6) And searching a searching model and a searching model for the interest points of the advertisement strategy based on the interest point searching result and the searching labels.

Further, for step S112, in the process of obtaining the corresponding advertisement drainage skip sub-graph based on the advertisement drainage search segment, the corresponding advertisement drainage skip sub-graph corresponding to the advertisement drainage search segment may be obtained from the preset search partition set.

On the basis, in the step, in the process of inputting the interest point retrieval content into the retrieval model and obtaining the visitor interest data of the advertisement drainage skip subgraph under the advertisement push feedback information through the retrieval model, the target interest point retrieval content can be input into the retrieval model, and the retrieval advertisement drainage search is carried out on the target interest point retrieval content through the retrieval model to obtain the visitor interest data of the target interest point retrieval content.

Further, in a possible implementation manner, regarding step S114, in the process of performing the interest point search on the multiple advertisement attention contents output by the multiple interest point search units to obtain the interest point search content, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1143 of obtaining a plurality of advertisement attention contents output by the plurality of interest point retrieval units and determining a target advertisement attention content of a global advertisement drainage path in the plurality of advertisement attention contents.

And a substep S1144 of performing interest point retrieval on other advertisement attention contents except the target advertisement attention content in the plurality of advertisement attention contents, wherein the advertisement drainage path of the other advertisement attention contents after the interest point retrieval is the same as the advertisement drainage path of the target advertisement attention content.

And a substep S1145 of listing other advertisement attention contents and target advertisement attention contents after the interest point retrieval to obtain the interest point retrieval contents.

Fig. 3 is a schematic diagram of functional modules of the advertisement pushing device 300 based on the block chain and the artificial intelligence according to the embodiment of the present disclosure, and this embodiment may divide the functional modules of the advertisement pushing device 300 based on the block chain and the artificial intelligence according to the method embodiment executed by the big data mining center 100, that is, the following functional modules corresponding to the advertisement pushing device 300 based on the block chain and the artificial intelligence may be used to execute the method embodiments executed by the big data mining center 100. The advertisement push device 300 based on the blockchain and the artificial intelligence may include a first obtaining module 310, a second obtaining module 320, a first generating module 330, and a second generating module 340, and the functions of the functional modules of the advertisement push device 300 based on the blockchain and the artificial intelligence are described in detail below.

The first obtaining module 310 is configured to obtain visitor interest data corresponding to advertisement push feedback information of a target advertisement push task, and perform crowd interest point retrieval based on the visitor interest data. The first obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the first obtaining module 310, reference may be made to the detailed description of the step S110.

The second obtaining module 320 is configured to obtain crowd interest point information corresponding to each crowd interest point in a preset number of crowd interest points, where the crowd interest point information includes crowd interest point coverage tags, interest point advertisement tags, and access attribute information, the crowd interest point coverage tags are used for the crowd interest points covered by the crowd interest points, the interest point advertisement tags are used for indicating advertisement attention contents of the crowd interest points, and the access attribute information is used for indicating advertisement access conditions in the crowd interest points. The second obtaining module 320 may be configured to perform the step S120, and for a detailed implementation of the second obtaining module 320, reference may be made to the detailed description of the step S120.

The first generating module 330 is configured to generate advertisement push optimization information corresponding to each crowd interest point according to the crowd interest point information corresponding to each crowd interest point, where the advertisement push optimization information is used to perform advertisement delivery policy node optimization processing or advertisement delivery policy node enhancement processing on advertisement policy start information, and the advertisement push optimization information and the crowd interest points have a one-to-one correspondence relationship. The first generating module 330 may be configured to execute the step S130, and the detailed implementation of the first generating module 330 may refer to the detailed description of the step S130.

A second generating module 340, configured to process the advertisement policy initiation information corresponding to each crowd interest point by using the advertisement push optimization information corresponding to each crowd interest point to obtain advertisement policy confirmation information corresponding to each crowd interest point, and generate an advertisement push control process for pushing an advertisement through a block chain according to the advertisement policy confirmation information corresponding to each crowd interest point, where the advertisement push optimization information, the advertisement policy initiation information, and the advertisement policy confirmation information have a one-to-one correspondence relationship. The second generating module 340 may be configured to execute the step S140, and for a detailed implementation of the second generating module 340, reference may be made to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the first obtaining module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the first obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

Fig. 4 illustrates a hardware structure diagram of a big data mining center 100 for implementing the above-mentioned advertisement push method based on block chains and artificial intelligence, according to an embodiment of the present disclosure, as shown in fig. 4, the big data mining center 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the first obtaining module 310, the second obtaining module 320, the first generating module 330, and the second generating module 340 included in the block chain and artificial intelligence based advertisement push apparatus 300 shown in fig. 3), so that the processor 110 may execute the block chain and artificial intelligence based advertisement push method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control transceiving actions of the transceiver 140, so as to perform data transceiving with the aforementioned advertisement service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the big data mining center 100, which implement principles and technical effects are similar, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the present application further provides a readable storage medium, where the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method for pushing an advertisement based on a blockchain and artificial intelligence is implemented as above.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences are processed, the use of alphanumeric characters, or the use of other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An interest retrieval method based on a block chain and artificial intelligence is applied to a big data mining center which is in communication connection with a plurality of advertisement service terminals, and comprises the following steps:

performing interest point retrieval on a plurality of advertisement attention contents output by the interest point retrieval units to obtain interest point retrieval contents;

determining a retrieval model corresponding to the advertisement push feedback information, inputting the interest point retrieval content into the retrieval model, obtaining visitor interest data of the advertisement drainage jumping subgraph under the advertisement push feedback information through the retrieval model, and retrieving the crowd interest point based on the visitor interest data of the advertisement drainage jumping subgraph under the advertisement push feedback information.

2. The interest retrieval method based on the block chain and the artificial intelligence as claimed in claim 1, wherein the step of inputting the user interest learning information into a plurality of interest point retrieval units in an interest point retrieval model respectively, and obtaining at least one advertisement attention content by performing at least one advertisement attention content matching through the interest point retrieval units comprises:

respectively inputting the user interest learning information into a plurality of interest point retrieval units in an interest point retrieval model;

for one interest point retrieval unit, performing advertisement attention content matching on corresponding user interest learning information through the interest point retrieval unit, acquiring second advertisement attention content extracted by other interest point retrieval units of the interest point retrieval units and performing partition service matching on the first advertisement attention content after the first advertisement attention content is obtained through advertisement attention content matching, and continuing to perform advertisement attention content matching based on a partition service matching result so as to alternately perform advertisement attention content matching and partition service matching;

the step of determining user interest learning information of a plurality of advertisement drainage paths based on the advertisement drainage jumping subgraph comprises the following steps;

and the index matching advertisement drainage jumping subgraph aims at the retrieval information nodes of each advertisement drainage path, and all the retrieval information nodes are spliced according to the incidence relation among the retrieval information nodes, so that the user interest learning information of a plurality of different advertisement drainage paths is obtained.

3. The blockchain and artificial intelligence based interest retrieval method of claim 1, further comprising:

wherein the method further comprises:

4. The blockchain and artificial intelligence based interest retrieval method according to claim 1, the user interest learning information includes at least first, second and third user interest learning information, the interest point retrieval model comprises a first interest point retrieval unit, a second interest point retrieval unit and a third interest point retrieval unit, wherein the first user interest learning information, the second user interest learning information and the third user interest learning information respectively correspond to the user interest learning information of the interest coverage learning segment, the interest data source segment and the interest output source segment, the first interest point retrieval unit, the second interest point retrieval unit and the third interest point retrieval unit respectively correspond to the interest point retrieval units of the interest coverage learning segment, the interest data source segment and the interest output source segment;

5. The blockchain and artificial intelligence based interest retrieval method of claim 1, further comprising:

6. A big data mining center, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one advertisement service terminal, the machine-readable storage medium is configured to store a program, instructions, or code, and the processor is configured to execute the program, instructions, or code in the machine-readable storage medium to perform the block chain and artificial intelligence based interest retrieval method of any one of claims 1-5.