CN112905881A

CN112905881A - Information pushing method and information pushing system based on block chain offline payment

Info

Publication number: CN112905881A
Application number: CN202110176401.2A
Authority: CN
Inventors: 张富平
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-09-01
Filing date: 2020-09-01
Publication date: 2021-06-04
Also published as: CN112069406B; CN112905880A; CN112069406A

Abstract

The embodiment of the application provides an information pushing method and an information pushing system based on block chain offline payment, user subscription label information of an information pushing request is obtained through the information pushing request based on offline payment sent by a digital financial service terminal, then information is mined according to big data of a subscription label item, a business pushing object list corresponding to the subscription label item is determined, and therefore business pushing directional content corresponding to the business pushing object list is pushed to the digital financial service terminal. Therefore, the oriented content can be effectively pushed to the business corresponding to the business pushing object list pushed by the digital financial service terminal according to the offline bill data condition of the offline payment scene, and the coverage of information pushing is improved.

Description

Information pushing method and information pushing system based on block chain offline payment

Technical Field

The application relates to the technical field of big data, in particular to an information pushing method and an information pushing system based on block chain offline payment.

Background

With the development of mobile internet technology and digital currency operation, digital currency will gradually become a new dominant payment mode in the future, and not only can support online payment, but also can support offline payment in an offline network state as in the current cash transaction.

In the offline payment state, various service bill data generated in the payment process cannot be synchronized in the cloud service platform in real time, and the offline payment scene can also reflect behavior characteristics of an offline wide user, so that subsequent service push service still needs to be provided for the offline bill data condition of the offline payment scene.

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 information push method and an information push system based on block chain offline payment, which can effectively push directional content to a service corresponding to a service push object list pushed by a digital financial service terminal according to an offline billing data condition in an offline payment scenario, thereby improving coverage of information push.

In a first aspect, the present application provides an information push method based on blockchain offline payment, which is applied to a cloud service push platform, where the cloud service push platform is in communication connection with a plurality of digital financial service terminals, and the method includes:

receiving an information pushing request based on offline payment sent by the digital financial service terminal, and acquiring user subscription label information of the information pushing request; wherein the user subscription tag information comprises subscription tag items;

determining a business push object list corresponding to the subscription tag item according to big data mining information of the subscription tag item, wherein the big data mining information is obtained by carrying out big data mining on an offline bill data set generated by a digital financial service terminal in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set by the cloud service push platform;

and pushing the business push directional content corresponding to the business push object list to the digital financial service terminal.

In a possible implementation manner of the first aspect, the step of determining, according to the big data mining information of the subscription tag item, a service push object list corresponding to the subscription tag item includes:

acquiring potential demand information and demand service scene information of the potential demand information from big data mining information of the subscription tag item; the demand analysis templates used by the potential demand information and the demand service scene information are first demand analysis templates;

processing the potential demand information according to the demand service scene information to generate demand extension information of the potential demand information;

performing theme-oriented feature extraction on the potential demand information and the demand extension information, and determining second theme feature information corresponding to first theme feature information corresponding to the demand extension information from the extracted current theme-oriented feature information;

performing feature fusion on the first theme feature information and the second theme feature information to obtain third theme feature information;

outputting target theme distribution information corresponding to the potential demand information according to the third theme characteristic information; and the requirement analysis template used by the target theme distribution information is a second requirement analysis template.

In a possible implementation manner of the first aspect, the step of processing the potential demand information according to the demand service scenario information to generate demand extension information of the potential demand information includes:

performing theme-oriented feature extraction on the potential demand information, performing theme entity object identification on a first theme-oriented feature corresponding to the obtained potential demand information, and obtaining a first entity object set corresponding to the potential demand information according to the identified theme entity object;

performing theme-oriented feature extraction on the demand service scene information, performing theme entity object identification on a second theme-oriented feature corresponding to the acquired demand service scene information, and acquiring a second entity object set corresponding to the demand service scene information according to the identified theme entity object;

acquiring first entity relationship information stored in the first entity object set, and converting the first entity relationship information into a corresponding first entity relationship vector;

obtaining second entity relationship information respectively stored by a plurality of entity key nodes in the second entity object set, and converting each second entity relationship information into a corresponding second entity relationship vector;

calculating a vector inner product result of each second entity relationship vector and the first entity relationship vector;

sorting the vector inner product result corresponding to each second entity relationship vector, and selecting a plurality of similar entity relationship vectors from the second entity relationship vectors according to the sorting result;

performing convolution processing on the similar entity relationship vectors to obtain convolution relationship vectors;

carrying out entity service coverage contact ratio calculation on the first entity object set and the second entity object set, and obtaining an influence factor vector according to the entity service coverage contact ratio obtained through calculation; the influence factor vector comprises a weight parameter corresponding to each entity key node in the second entity object set;

calculating the vector inner product of the convolution relationship vector and the influence factor vector, and taking the calculated result as the expansion service vector of the first entity relationship information;

adding the expanded service vector to a potential demand unit set in the potential demand information to obtain initial expanded data;

performing subject entity object identification on the initial extension data to obtain a reference subject entity object;

and obtaining the demand extension information corresponding to the potential demand information according to the first entity object set, the second entity object set and the reference subject entity object.

In a possible implementation manner of the first aspect, the step of extracting theme-oriented features of the potential demand information and the demand extension information, and determining second theme feature information corresponding to first theme feature information corresponding to the demand extension information from the extracted current theme-oriented feature information includes:

performing theme-oriented feature extraction on the potential demand information and the demand extension information to obtain current theme-oriented feature information mapped in theme-oriented features of the potential demand information and the demand extension information; the current theme-oriented feature information comprises distribution feature information of a plurality of theme feature nodes;

determining similar distribution characteristic information of the first topic characteristic information from distribution characteristic information of a plurality of topic characteristic nodes contained in the current topic-oriented characteristic information, and taking the similar distribution characteristic information as the second topic characteristic information.

In a possible implementation manner of the first aspect, the step of performing feature fusion on the first topic feature information and the second topic feature information to obtain third topic feature information includes:

inputting the first theme feature information and the second theme feature information into a preset theme distribution analysis model respectively, so that the theme distribution analysis model outputs effective theme feature information of the first theme feature information and the second theme feature information respectively to obtain first target theme feature information and second target theme feature information;

performing convolution calculation on the first target theme characteristic information to obtain first theme convolution information; performing theme-oriented feature extraction on the first target theme feature information, performing convolution calculation on the extracted theme feature information to obtain second theme convolution information, and calculating a dot product of the first theme convolution information and the second theme convolution information to obtain a first theme feature set corresponding to the first target theme feature information;

performing convolution calculation on the second target theme characteristic information to obtain third theme convolution information; performing theme-oriented feature extraction on the second target theme feature information, performing convolution calculation on the extracted theme feature information to obtain fourth theme convolution information, and calculating a dot product of the third theme convolution information and the fourth theme convolution information to obtain a second theme feature set corresponding to the second target theme feature information;

and calculating a fusion feature set of the first topic feature set and the second topic feature set, and taking the obtained fusion feature set as the third topic feature information.

In a possible implementation manner of the first aspect, the step of outputting, according to the third topic feature information, target topic distribution information corresponding to the potential demand information includes:

obtaining a plurality of topic distribution description vectors in the third topic feature information and a topic distribution analysis strategy corresponding to each topic distribution description vector, where the topic distribution description vectors include a first topic distribution description vector and a second topic distribution description vector, where the first topic distribution description vector and the second topic distribution description vector are pairs of mapping associated topic distribution description vectors;

encoding the third topic feature information to output a first basic encoding vector corresponding to each first topic distribution description vector and a target encoding vector corresponding to the third topic feature information;

calculating the association degree between the target coding vector and each first basic coding vector to obtain the coding vector proximity degree between each corresponding first theme distribution description vector and the third theme characteristic information;

identifying all description elements in each first theme distribution description vector and the corresponding business description weight of each first theme distribution description vector;

generating a theme distribution clustering node of a corresponding theme distribution description vector according to all the description elements and the service description weight;

generating a theme distribution clustering range corresponding to each theme distribution description vector according to the theme distribution analysis strategy;

obtaining a first clustering group of each topic distribution description vector by using each topic distribution clustering node corresponding to the topic distribution clustering range;

clustering the first basic coding vector corresponding to each topic distribution description vector according to the second topic distribution description vector to obtain a second clustering group of each topic distribution description vector;

determining a target topic distribution description vector in the plurality of topic distribution description vectors according to the first clustering group and the second clustering group;

and taking the first basic coding vector corresponding to the target topic distribution description vector as a second coding vector, and adding the second coding vector to the third topic feature information to output target topic distribution information corresponding to the potential demand information.

In a possible implementation manner of the first aspect, the topic distribution parsing policy includes a vector structure clustering policy on the topic distribution description vector and a feature value clustering policy corresponding to the topic distribution description vector;

the step of generating the topic distribution clustering range corresponding to each topic distribution description vector according to the topic distribution analysis strategy comprises the following steps:

carrying out characteristic value clustering on the theme distribution description vector according to the characteristic value clustering strategy to generate a theme distribution information characteristic value corresponding to the theme distribution description vector;

carrying out vector structure clustering on the theme distribution description vector according to the vector structure clustering strategy to generate a vector structure clustering result corresponding to the theme distribution description vector;

and generating a theme distribution clustering range corresponding to the theme distribution description vector according to the theme distribution information characteristic value and the vector structure clustering result.

In a possible implementation manner of the first aspect, the topic distribution description vector further includes a third topic distribution description vector;

before the step of obtaining the respective first cluster group of each topic distribution description vector by using each topic distribution cluster node corresponding to the topic distribution cluster range, the method further includes:

calculating the coding translation parameter of each reference coding object in the reference coding object list corresponding to each first basic coding vector relative to the third topic distribution description vector;

fusing all the coding translation parameters corresponding to each first basic coding vector to obtain a fused coding parameter corresponding to each first basic coding vector;

arranging all the first basic coding vectors in sequence according to the fusion coding parameters corresponding to each first basic coding vector, and determining the respective priority parameters of each first basic coding vector according to the arranged sequence of each first basic coding vector;

processing the fusion coding parameter corresponding to each first basic coding vector according to the priority parameter of each first basic coding vector to generate a weighted fusion coding parameter of each topic distribution description vector;

the step of obtaining the respective first cluster group of each topic distribution description vector by using each topic distribution cluster node corresponding to the topic distribution cluster range includes:

and clustering the weighted fusion coding parameters of each topic distribution description vector by using each topic distribution clustering node corresponding to the topic distribution clustering range to obtain a first clustering group corresponding to each topic distribution description vector.

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

acquiring big data mining information of the subscription tag item;

the step of obtaining big data mining information of the subscription tag item includes:

acquiring an offline bill data set generated by the digital financial service terminal in a blockchain offline payment environment and a target payment environment element corresponding to the offline bill data set from the digital financial service terminal;

acquiring excavatable target services of the service tags to be excavated under the target payment environment elements, grouping the excavatable target services under each target payment environment element according to a preset subscribed push group, and respectively generating an excavatable target service set of each subscribed push group;

acquiring, for each subscribed push group, knowledge graph data of each mineable target service in the mineable target service set of the subscribed push group, which is matched with the offline bill data set, and performing big data mining on the knowledge graph data set of each subscribed push group based on a push service graph corresponding to the subscribed push group to obtain big data mining information of each subscribed push group;

and obtaining the big data mining information of the target subscribed push group included in the subscription tag item from the big data mining information of each subscribed push group.

In a second aspect, an embodiment of the present application further provides an information pushing apparatus based on blockchain offline payment, which is applied to a cloud service pushing platform, where the cloud service pushing platform is in communication connection with a plurality of digital financial service terminals, and the apparatus includes:

the receiving module is used for receiving an information pushing request based on offline payment sent by the digital financial service terminal and obtaining user subscription label information of the information pushing request; wherein the user subscription tag information comprises subscription tag items;

the determining module is used for determining a business push object list corresponding to the subscription tag item according to big data mining information of the subscription tag item, wherein the big data mining information is obtained by carrying out big data mining on an offline bill data set generated by a digital financial service terminal in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set by the cloud service push platform;

and the pushing module is used for pushing the business pushing directional content corresponding to the business pushing object list to the digital financial service terminal.

In a third aspect, an embodiment of the present application further provides an information pushing system based on blockchain offline payment, where the information pushing system based on blockchain offline payment includes a cloud service pushing platform and a plurality of digital financial service terminals in communication connection with the cloud service pushing platform;

the cloud service pushing platform is used for receiving an information pushing request based on offline payment sent by the digital financial service terminal and obtaining user subscription label information of the information pushing request; wherein the user subscription tag information comprises subscription tag items;

the cloud service push platform is used for determining a business push object list corresponding to the subscription tag item according to big data mining information of the subscription tag item, wherein the big data mining information is obtained by carrying out big data mining on an offline bill data set generated by a digital financial service terminal in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set by the cloud service push platform;

and the cloud service pushing platform is used for pushing the business pushing directional content corresponding to the business pushing object list to the digital financial service terminal.

In a fourth aspect, an embodiment of the present application further provides a cloud service pushing platform, where the cloud service pushing platform 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 used for being in communication connection with at least one digital financial service terminal, the machine-readable storage medium is used for storing a program, an instruction, or a code, and the processor is used for executing the program, the instruction, or the code in the machine-readable storage medium to execute the information pushing method based on block chain offline payment in the first aspect or any one of 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 information pushing method based on blockchain offline payment in the first aspect or any one of the possible implementations of the first aspect.

Based on any one of the above aspects, the information pushing request based on offline payment sent by the digital financial service terminal is used for obtaining the user subscription tag information of the information pushing request, and then information is mined according to the big data of the subscription tag item to determine the service pushing object list corresponding to the subscription tag item, so that the oriented content is pushed to the service corresponding to the service pushing object list of the digital financial service terminal. Therefore, the oriented content can be effectively pushed to the business corresponding to the business pushing object list pushed by the digital financial service terminal according to the offline bill data condition of the offline payment scene, and the coverage of information pushing is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be 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 it will be apparent to those skilled in the art that other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic application scenario diagram of an information push system based on offline payment of a blockchain according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of an information pushing method based on offline payment with blockchain according to an embodiment of the present disclosure;

fig. 3 is a functional module diagram of an information pushing apparatus based on offline payment of a blockchain according to an embodiment of the present disclosure;

fig. 4 is a schematic block diagram of structural components of a cloud service push platform for implementing the information push method based on blockchain offline payment according to the embodiment of the present application.

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 information push system 10 based on offline payment of a blockchain according to an embodiment of the present application. The information push system 10 based on blockchain offline payment may include a cloud service push platform 100 and a digital financial service terminal 200 communicatively connected to the cloud service push platform 100. The information push system 10 based on blockchain offline payment shown in fig. 1 is only one possible example, and in other possible embodiments, the information push system 10 based on blockchain offline payment may also include only one of the components shown in fig. 1 or may also include other components.

In this embodiment, the digital financial services terminal 200 may comprise a mobile device, a tablet computer, a laptop computer, etc., or any combination thereof. In some embodiments, the mobile device may include an internet of things device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the internet of things device may include a control device of a smart appliance device, a smart monitoring device, a smart television, a smart camera, and the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistant, a gaming device, and the like, or any combination thereof. In some embodiments, the virtual reality device and the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, virtual reality devices and augmented reality devices may include various virtual reality products and the like.

In this embodiment, the cloud service push platform 100 and the digital financial service terminal 200 in the information push system 10 based on blockchain offline payment may cooperatively perform the information push method based on blockchain offline payment described in the following method embodiment, and for a specific step part of the cloud service push platform 100 and the digital financial service terminal 200, reference may be made to the detailed description of the following method embodiment.

Based on the inventive concept of the technical scheme provided by the application, the cloud service push platform 100 provided by the application can be applied to scenes such as smart medical treatment, smart city management, smart industrial internet, general service monitoring management and the like, which can apply a big data technology or a cloud computing technology, and the like, and can also be applied to scenes such as but not limited to new energy automobile system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud automobile management platform, block chain financial data service platform and the like, but is not limited thereto.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of an information pushing method based on blockchain offline payment according to an embodiment of the present disclosure, where the information pushing method based on blockchain offline payment according to the present disclosure may be executed by the cloud service pushing platform 100 shown in fig. 1, and the information pushing method based on blockchain offline payment is described in detail below.

Step S110, receiving an information push request based on offline payment sent by the digital financial service terminal 200, and obtaining the user subscription tag information of the information push request.

And step S120, determining a service push object list corresponding to the subscription label item according to the big data mining information of the subscription label item.

Step S130, pushing the service push oriented content corresponding to the service push object list to the digital financial service terminal 200.

In this embodiment, the user subscription tag information may include, for example, a subscription tag item, where the subscription tag item may be used to indicate a type of a subscribed service category, and may be specifically selected and configured in advance by a user of the digital financial services terminal 200, which is not limited in detail herein.

In this embodiment, the big data mining information may be big data mining information obtained by a cloud service push platform performing big data mining on an offline bill data set generated by the digital financial service terminal 200 in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set. The offline billing data may refer to a scenario that, for each offline payment, a plurality of billing statistics boards are usually included, and may include, for example and without limitation, billing content, billing scenario, billing situation, and the like. The target payment environment element may be used to represent an environment element obtained offline in a specific payment process, such as a payment service scenario type, a user type of a payment user, and the like.

Based on the above steps, in this embodiment, the user subscription tag information of the information push request is obtained through the information push request based on offline payment sent by the digital financial service terminal 200, and then the information is mined according to the big data of the subscription tag item, so as to determine the service push object list corresponding to the subscription tag item, thereby pushing the service push directional content corresponding to the service push object list to the digital financial service terminal 200. Therefore, the oriented content of the business push corresponding to the business push object list can be effectively pushed to the digital financial service terminal 200 according to the offline bill data condition of the offline payment scene, and the coverage of information push is improved.

In one possible implementation, step S120 may be implemented by the following exemplary substeps, which are described in detail below.

And a substep S121, obtaining the potential demand information and the demand service scene information of the potential demand information from the big data mining information of the subscription label item. The demand analysis templates used by the potential demand information and the demand service scenario information are both first demand analysis templates.

And a substep S122, processing the potential demand information according to the demand service scene information, and generating demand extension information of the potential demand information.

And a substep S123 of performing theme-oriented feature extraction on the potential demand information and the demand extension information, and determining second theme feature information corresponding to the first theme feature information corresponding to the demand extension information from the extracted current theme-oriented feature information.

And a substep S124 of performing feature fusion on the first theme feature information and the second theme feature information to obtain third theme feature information.

And a substep S125, outputting target topic distribution information corresponding to the potential demand information according to the third topic feature information.

For example, the requirement analysis template used by the target topic distribution information is a second requirement analysis template. Therefore, topic distribution conversion can be carried out based on different requirement analysis templates, so that different types of requirement analysis templates can be defined by a user during subsequent pushing.

Exemplarily, in the sub-step S122, it can be realized by the following specific embodiments.

(1) And performing theme-oriented feature extraction on the potential demand information, performing theme entity object identification on the first theme-oriented feature corresponding to the obtained potential demand information, and obtaining a first entity object set corresponding to the potential demand information according to the identified theme entity object.

(2) And performing theme-oriented feature extraction on the demand service scene information, performing theme entity object identification on a second theme-oriented feature corresponding to the acquired demand service scene information, and acquiring a second entity object set corresponding to the demand service scene information according to the identified theme entity object.

(3) Obtaining first entity relation information stored in the first entity object set, and converting the first entity relation information into a corresponding first entity relation vector.

(4) And obtaining second entity relationship information respectively stored by a plurality of entity key nodes in the second entity object set, and converting each second entity relationship information into a corresponding second entity relationship vector.

(5) And calculating the vector inner product result of each second entity relationship vector and the first entity relationship vector.

(6) And sorting the vector inner product result corresponding to each second entity relationship vector, and selecting a plurality of similar entity relationship vectors from the plurality of second entity relationship vectors according to the sorting result.

(7) And performing convolution processing on the plurality of similar entity relationship vectors to obtain convolution relationship vectors.

(8) And calculating the entity service coverage coincidence degree of the first entity object set and the second entity object set, and obtaining an influence factor vector according to the calculated entity service coverage coincidence degree. The influence factor vector includes a weight parameter corresponding to each entity key node in the second entity object set.

(9) And calculating the vector inner product of the convolution relation vector and the influence factor vector, and taking the calculated result as the expansion service vector of the first entity relation information.

(10) And adding the expanded service vector to a potential demand unit set in the potential demand information to obtain initial expanded data.

(11) And performing subject entity object identification on the initial extension data to obtain a reference subject entity object.

(12) And obtaining demand extension information corresponding to the potential demand information according to the first entity object set, the second entity object set and the reference subject entity object.

Exemplarily, in the sub-step S123, it can be realized by the following specific embodiments.

(1) And extracting theme-oriented features of the potential demand information and the demand extension information to obtain current theme-oriented feature information mapped in the theme-oriented features of the potential demand information and the demand extension information.

In this embodiment, the current theme-oriented feature information includes distribution feature information of a plurality of theme feature nodes.

(2) And determining similar distribution characteristic information of the first topic characteristic information from the distribution characteristic information of the plurality of topic characteristic nodes contained in the current topic-oriented characteristic information, and taking the similar distribution characteristic information as second topic characteristic information.

Exemplarily, in the sub-step S124, it can be realized by the following specific embodiments.

(1) And respectively inputting the first theme characteristic information and the second theme characteristic information into a preset theme distribution analysis model so that the theme distribution analysis model respectively outputs the respective effective theme characteristic information of the first theme characteristic information and the second theme characteristic information to obtain first target theme characteristic information and second target theme characteristic information.

(2) And performing convolution calculation on the first target theme characteristic information to obtain first theme convolution information. And performing theme-oriented feature extraction on the first target theme feature information, performing convolution calculation on the extracted theme feature information to obtain second theme convolution information, and calculating the dot product of the first theme convolution information and the second theme convolution information to obtain a first theme feature set corresponding to the first target theme feature information.

(3) And performing convolution calculation on the second target theme characteristic information to obtain third theme convolution information. And performing theme-oriented feature extraction on the second target theme feature information, performing convolution calculation on the extracted theme feature information to obtain fourth theme convolution information, and calculating the dot product of the third theme convolution information and the fourth theme convolution information to obtain a second theme feature set corresponding to the second target theme feature information.

(4) And calculating a fusion feature set of the first topic feature set and the second topic feature set, and taking the obtained fusion feature set as third topic feature information.

Exemplarily, in the sub-step S125, it can be realized by the following specific embodiments.

(1) And acquiring a plurality of topic distribution description vectors in the third topic feature information and a topic distribution analysis strategy corresponding to each topic distribution description vector, wherein the topic distribution description vectors comprise a first topic distribution description vector and a second topic distribution description vector, and the first topic distribution description vector and the second topic distribution description vector are mapping association topic distribution description vector pairs existing between each other.

(2) And coding the third topic characteristic information to output a first basic coding vector corresponding to each first topic distribution description vector and a target coding vector corresponding to the third topic characteristic information.

(3) And calculating the association degree between the target coding vector and each first basic coding vector to obtain the coding vector proximity degree between each corresponding first theme distribution description vector and the third theme characteristic information.

(4) All description elements in each first theme distribution description vector and the corresponding business description weight of each first theme distribution description vector are identified.

(5) And generating a theme distribution clustering node of the corresponding theme distribution description vector according to all the description elements and the service description weight.

(6) And generating a theme distribution clustering range corresponding to each theme distribution description vector according to the theme distribution analysis strategy.

As an example, the topic distribution parsing policy may include a vector structure clustering policy for topic distribution description vectors and a feature value clustering policy for topic distribution description vectors. Based on the theme distribution description vector clustering method, the theme distribution description vector can be subjected to characteristic value clustering according to the characteristic value clustering strategy to generate theme distribution information characteristic values corresponding to the theme distribution description vector, then the theme distribution description vector can be subjected to vector structure clustering according to the vector structure clustering strategy to generate vector structure clustering results corresponding to the theme distribution description vector, and therefore a theme distribution clustering range corresponding to the theme distribution description vector can be generated according to the theme distribution information characteristic values and the vector structure clustering results.

(7) And obtaining a respective first clustering group of each topic distribution description vector by using each topic distribution clustering node corresponding to the topic distribution clustering range.

(8) And clustering the first basic coding vector corresponding to each topic distribution description vector according to the second topic distribution description vector to obtain a second clustering group of each topic distribution description vector.

(9) And determining a target topic distribution description vector in the plurality of topic distribution description vectors according to the first clustering group and the second clustering group.

(10) And taking the first basic coding vector corresponding to the target topic distribution description vector as a second coding vector, and adding the second coding vector into the third topic characteristic information to output target topic distribution information corresponding to the potential demand information.

Exemplarily, the topic distribution description vector further includes a third topic distribution description vector, and before (7), the encoding translation parameter of each first basic encoding vector with respect to each reference encoding object in the reference encoding object list corresponding to the third topic distribution description vector may be further calculated, and then all the encoding translation parameters corresponding to each first basic encoding vector are fused to obtain a fused encoding parameter corresponding to each first basic encoding vector.

On this basis, all the first basic coding vectors may be arranged in sequence according to the fusion coding parameter corresponding to each first basic coding vector, the respective priority parameter of each first basic coding vector is determined according to the arranged sequence of each first basic coding vector, and then the fusion coding parameter corresponding to each first basic coding vector is processed according to the respective priority parameter of each first basic coding vector, so as to generate the weighted fusion coding parameter of each topic distribution description vector.

Thus, in (7), the weighted fusion coding parameters of each topic distribution description vector may be clustered by using each topic distribution clustering node corresponding to the topic distribution clustering range, so as to obtain a first clustering group corresponding to each topic distribution description vector.

In a possible implementation manner, before step S120, the information pushing method based on blockchain offline payment according to this embodiment may further include step S101 of obtaining big data mining information of the subscription tag item.

For example, step S101 may be specifically realized by the following substeps.

In sub-step S1011, an offline billing data set generated by each digital financial service terminal 200 in the blockchain offline payment environment and a target payment environment element corresponding to the offline billing data set are obtained from each digital financial service terminal 200.

The target payment environment element may be used to represent an environment element obtained offline in a specific payment process, such as a payment service scenario type, a user type of a payment user, and the like.

Step S1012, acquiring the mineable target service of the service tag to be mined under the target payment environment element, grouping the mineable target service under each target payment environment element according to a predetermined subscribed push group, and generating a mineable target service set of each subscribed push group respectively.

The offline billing data may refer to a scenario that, for each offline payment, a plurality of billing statistics boards are usually included, and may include, for example and without limitation, billing content, billing scenario, billing situation, and the like.

The mineable target service can be used for representing a mining type label of a specific application of the service label to be mined under each target payment environment element of the offline bill data, such as a fresh knowledge new mining type label, a digital product knowledge new mining type label and the like.

In this embodiment, the predetermined subscribed push group may be flexibly selected according to actual design requirements, and is mainly used to represent a subscription push selection menu provided for different users, which is not limited herein in detail.

Step S1013, for each subscribed push group, acquiring the knowledge graph data of each mineable target service in the mineable target service set of the subscribed push group, which matches the offline bill data set, and performing big data mining on the knowledge graph data set of each subscribed push group based on the push service portrait corresponding to the subscribed push group to obtain big data mining information of each subscribed push group;

step S1014, obtaining the big data mining information of the target subscribed push group included in the subscription tag item from the big data mining information of each subscribed push group.

Based on the above steps, the mining-capable target service of the service tag to be mined under the target payment environment element corresponding to the offline bill data set is considered, and then the mining-capable target service under each target payment environment element is grouped based on the predetermined subscribed push group, so that differences between different target payment environment elements and the subscribed push groups are considered, and thus, the big data mining is performed on the knowledge graph data set of each subscribed push group based on the push service graph corresponding to the subscribed push group, the accuracy of the big data mining can be effectively improved, and the big data mining result can be more matched with an actual service scene.

In one possible implementation, such as for step S1013, in the process of obtaining the knowledge-graph data that each mineable target service in the set of mineable target services of the subscribed push group matches the offline billing data set, the following exemplary sub-steps may be further implemented, which are described in detail below.

And a substep S10131, obtaining a matching keyword vector related to each mineable target service in the mineable target service set subscribed to the push packet.

And a substep S10132 of matching the corresponding bill plate contents from the offline bill data set according to the matching keyword vector associated with each mineable target service.

And a substep S10133 of determining, according to the knowledge-graph content corresponding to each service record plate in the bill plate content matched with the matching keyword vector related to each mineable target service, that each mineable target service in the mineable target service set of the subscribed push packet matches the knowledge-graph data of the offline bill data set.

In one possible implementation, for example, for step S1013, in the process of performing big data mining on the knowledge-graph data set of each subscribed push group based on the push service graph corresponding to the subscribed push group, the following sub-steps may be performed.

In step S10134, the push service portrait node parameters of each push service portrait node of each subscribed push group and the portrait activation content covered by the push service portrait node are determined based on the push service portrait corresponding to the subscribed push group.

In the substep S10135, mining flow parameters of the big data mining component required for big data mining of the push service portrait node in each subscribed push group are determined according to the push service portrait node parameters of the push service portrait node in each subscribed push group and the portrait activation content covered by the push service portrait node.

In the sub-step S10136, according to the mining flow parameter of the big data mining component required by each push service portrait node, each big data mining component is determined as a mining unit, and the operation configuration information corresponding to the mining unit is operation configuration information included in the push service portrait node and other than the currently configured operation configuration information of the subscribed push packet.

And a substep S10137 of establishing a mining business relationship of the mining unit according to the running configuration information corresponding to the mining unit, determining a business matching element of the mining business relationship, and obtaining preliminary mining information of the first mining unit in the business matching element for performing big data mining on each subscribed pushed and grouped knowledge graph data set.

And a substep S10138 of screening the primary mining information of the mining unit and the mining units behind the mining unit when screening the primary mining information of each mining unit behind the first mining unit in sequence according to the hierarchy of the mining unit, reestablishing the mining business relationship of the mining unit according to the screened primary mining information, determining the business matching elements of the reestablished mining business relationship, and obtaining the screened primary mining information of the mining unit in the business matching elements of the reestablished mining business relationship.

And a substep S10139 of obtaining the screened preliminary mining information of all the mining units, and taking the screened preliminary mining information of all the mining units as a big data mining result.

In one possible implementation, for example, after step S1013, the following steps may be further included:

step S1015, judging whether the extended loading subject feature information for indicating that the extensible loading service exists in the extensible target service exists in the big data mining process, and extracting a first knowledge graph of a first extensible target service corresponding to the extended loading subject feature information of the big data mining and at least one second knowledge graph of a second extensible target service having an extended loading service relation with the first extensible target service when the extended loading subject feature information is detected;

and step S1016, determining global big data mining information between the first knowledge graph and the at least one second knowledge graph according to a preset artificial intelligence model.

In one possible implementation, for example, for step S1015, a first knowledge graph of a first mineable target service corresponding to the extended loading topic feature information of the big data mining and a second knowledge graph of at least one second mineable target service having an extended loading business relationship with the first mineable target service may be extracted from the big data mining record information generated in the big data mining process. Wherein the at least one second mineable target service for which an extended load business relationship exists with the first mineable target service may refer to a second mineable target service for which a linkage effect associated with the first mineable target service exists.

For example, if a certain mineable target service needs to extend mining during the mining of a first mineable target service, then the mineable target service may be understood as a second mineable target service that has an extended load business relationship with the first mineable target service.

In one possible implementation, such as for step S1016, this may be achieved by the following exemplary substeps, described in detail below.

And a substep S10161 of fusing the knowledge graph nodes corresponding to the first knowledge graph and the at least one second knowledge graph according to each identical knowledge graph node to obtain a fused knowledge graph.

The sub-step S10162 adds the first knowledge-map and the at least one second knowledge-map to a preset data map classification queue, and establishes a plurality of first data map classification parameters of the first knowledge-map and a plurality of second data map classification parameters of the second knowledge-map based on the data map classification queue.

Substep S10163, determining first knowledge expression information of a first excavatable target service according to each first data map classification parameter, determining second knowledge expression information of a second excavatable target service according to each second data map classification parameter, mapping the first knowledge expression information and the second knowledge expression information to a knowledge entity feature model to obtain a first knowledge map feature corresponding to the first knowledge expression information and a second knowledge map feature corresponding to the second knowledge expression information, determining a plurality of knowledge corpus objects corresponding to a fusion knowledge map of the knowledge entity feature model, summarizing the plurality of knowledge corpus objects to obtain at least a plurality of different classes of knowledge corpus excavation lists, excavating first corpus characterization features corresponding to the first knowledge map feature and corresponding second knowledge map characterization features of each knowledge corpus object in the knowledge corpus excavation list in a preset big data excavation process aiming at each knowledge corpus excavation list The second corpus of features depicts features.

And a substep S10164 of merging the generated simulated mining streams according to the mining results of the first corpus portrait characterization feature and the second corpus portrait characterization feature corresponding to each knowledge corpus object in the knowledge corpus mining list and the preset priority of the knowledge expectation, restoring the merged simulated mining streams, and determining global big data mining information of the first excavatable target service and the at least one second excavatable target service.

In this way, subsequent big data mining can be performed with the associated mineable target service as an independent mining target in a targeted manner in the actual big data mining process.

In one possible implementation manner, for step S10164, for example, in the process of restoring the spliced simulated mining stream and determining the global big data mining information of the first mineable target service and the at least one second mineable target service, the spliced simulated mining stream may be reversely converted according to each corresponding simulated mining node to obtain the global big data mining information of the first mineable target service and the at least one second mineable target service.

Fig. 3 is a schematic diagram of functional modules of the information pushing apparatus 300 based on blockchain offline payment according to an embodiment of the present disclosure, in this embodiment, the information pushing apparatus 300 based on blockchain offline payment may be divided into the functional modules according to an embodiment of a method performed by the cloud service pushing platform 100, that is, the following functional modules corresponding to the information pushing apparatus 300 based on blockchain offline payment may be used to perform the method embodiments performed by the cloud service pushing platform 100. The device 300 for pushing information based on blockchain offline payment may include a receiving module 310, a determining module 320, and a pushing module 330, and the functions of the functional modules of the device 300 for pushing information based on blockchain offline payment are described in detail below.

A receiving module 310, configured to receive an information push request based on offline payment sent by the digital financial service terminal 200, and obtain user subscription tag information of the information push request; wherein the user subscription tag information comprises subscription tag items. The receiving module 310 may be configured to perform the step S110, and the detailed implementation of the receiving module 310 may refer to the detailed description of the step S110.

The determining module 320 is configured to determine, according to big data mining information of the subscription tag item, a service push object list corresponding to the subscription tag item, where the big data mining information is obtained by the cloud service push platform performing big data mining on an offline bill data set generated by the digital financial service terminal 200 in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set. The determining module 320 may be configured to perform the step S120, and the detailed implementation of the determining module 320 may refer to the detailed description of the step S120.

The pushing module 330 is configured to push the service push targeted content corresponding to the service push object list to the digital financial service terminal 200. The pushing module 330 may be configured to perform the step S130, and the detailed implementation manner of the pushing module 330 may refer to the detailed description of the step S130.

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 receiving module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the receiving 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.

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 illustrates a hardware structure diagram of the cloud service push platform 100 for implementing the control device, where the cloud service push platform 100 provided by the embodiment of the present disclosure may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140, as shown in fig. 4.

In an implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the receiving module 310, the determining module 320, and the pushing module 330 included in the information pushing apparatus 300 based on blockchain offline payment shown in fig. 3), so that the processor 110 may execute the information pushing method based on blockchain offline payment 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 the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned digital financial service terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the cloud service push platform 100, and implementation principles and technical effects thereof 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 disclosure also provides a readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the information push method based on the blockchain offline payment is implemented.

The readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. Readable storage media can be any available media that can be accessed by a general purpose or special purpose computer.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.

Claims

1. An information pushing method based on block chain offline payment is applied to a cloud service pushing platform, wherein the cloud service pushing platform is in communication connection with a plurality of digital financial service terminals, and the method comprises the following steps:

pushing the business pushing directional content corresponding to the business pushing object list to the digital financial service terminal;

the user subscription label information comprises a subscription label item, and the subscription label item is used for representing the subscribed service classification type;

the target payment environment element is used for representing an environment element acquired offline in a specific payment process, and the environment element comprises a payment service scene type and a user type of a payment user;

wherein the method further comprises:

acquiring big data mining information of the subscription tag item;

obtaining the big data mining information of the target subscribed push group included in the subscription tag item from the big data mining information of each subscribed push group;

wherein the mineable target service is used to represent mining type tags of specific applications of service tags to be mined under target payment environment elements of each offline bill data;

the predetermined subscribed push groups are used to characterize subscribed push selection menus provided for different users.

2. The method of claim 1, wherein the step of obtaining the knowledge-graph data that each mineable target service in the set of mineable target services subscribed to the push group matches the offline billing data set comprises:

acquiring a matched keyword vector related to each excavatable target service in the excavatable target service set subscribed to the push grouping;

matching corresponding bill plate contents from the offline bill data set according to the matching keyword vectors related to each mineable target service;

and determining that each mineable target service in the mineable target service set subscribed to the push group is matched with the knowledge-graph data of the offline bill data set according to the knowledge-graph content corresponding to each business record plate in the bill plate content matched with the matched keyword vector related to each mineable target service.

3. The information push method based on offline payment for the block chain according to claim 1, wherein the step of determining the service push object list corresponding to the subscription tag item according to the big data mining information of the subscription tag item includes:

4. The information pushing method based on blockchain offline payment according to claim 3, wherein the step of processing the potential demand information according to the demand service scenario information to generate demand extension information of the potential demand information includes:

5. The information pushing method based on blockchain offline payment according to claim 3, wherein the step of extracting theme-oriented features from the potential demand information and the demand extension information, and determining second theme feature information corresponding to the first theme feature information corresponding to the demand extension information from the extracted current theme-oriented feature information includes:

6. The information pushing method based on offline blockchain payment according to claim 3, wherein the step of performing feature fusion on the first topic feature information and the second topic feature information to obtain third topic feature information includes:

7. The information push method based on offline blockchain payment according to claim 3, wherein the step of outputting the target topic distribution information corresponding to the potential demand information according to the third topic feature information includes:

8. The information pushing method based on offline blockchain payment according to claim 7, wherein the topic distribution parsing strategy comprises a vector structure clustering strategy for the topic distribution description vector and a feature value clustering strategy corresponding to the topic distribution description vector;

9. The information push method based on blockchain offline payment according to claim 7, wherein the topic distribution description vector further comprises a third topic distribution description vector;

10. An information push system based on blockchain offline payment comprises a cloud service push platform and a plurality of digital financial service terminals in communication connection with the cloud service push platform;

the cloud service pushing platform is used for pushing business pushing directional contents corresponding to the business pushing object list to the digital financial service terminal;

the cloud service pushing platform is further used for acquiring big data mining information of the subscription tag item;

the method for acquiring the big data mining information of the subscription tag item comprises the following steps: