CN112199733B

CN112199733B - Information processing method based on block chain and cloud computing and digital financial service center

Info

Publication number: CN112199733B
Application number: CN202011017297.4A
Authority: CN
Inventors: 王海宏
Original assignee: Shanghai Newtouch Software Co ltd
Current assignee: SHANGHAI NEWTOUCH SOFTWARE Co.,Ltd.
Priority date: 2020-09-24
Filing date: 2020-09-24
Publication date: 2021-09-14
Anticipated expiration: 2040-09-24
Also published as: CN112199733A; CN112966066A; CN112966067A

Abstract

The embodiment of the application provides an information processing method based on a block chain and cloud computing and a digital financial service center, through determining payment object information in a payment record of a first target resource associated with a payment event, determining a first evidence storage element list, then determining a payment record of a second target resource and a corresponding second evidence storage element list, selecting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the second evidence storage element list, and then determining target payment object information matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource according to the obtained target evidence storage element list, and then carrying out information pushing. Therefore, the situation of iterative transfer of the payment records of the target resources associated with the payment event is considered, so that subsequent information mining analysis can be performed by combining the target payment object information of the iterative transfer, and the accuracy of subsequent information pushing is improved.

Description

Information processing method based on block chain and cloud computing and digital financial service center

Technical Field

The application relates to the technical field of block chains and cloud computing, in particular to an information processing method based on the block chains and the cloud computing and a digital financial service center.

Background

Currently, with the large-scale growth of the internet and intelligent mobile terminals, internet payment develops rapidly, the traditional internet payment process cannot guarantee the authenticity of transaction information provided by a platform, and there is a possibility that actions of counterfeiting and changing transactions to collect funds of merchants and users exist.

The block chain has the characteristics of decentralization, openness, autonomy and information non-falsification, can be used for constructing a supervision tool box which is required by a supervision department and comprises a plurality of means, is beneficial to implementing accurate, timely and more-dimensional supervision, solves the problem that online commercial service platforms and offline payment service providers are suspected to be clear, and is more common in transfer payment, so that the payment method based on the block chain technology is produced.

In the related art, the payment events generated in the blockchain verification process can reflect the payment habits and business service preferences of the user, so that mining analysis can be performed on the payment events, and more useful service information can be provided for the user. However, the inventor researches and discovers that in the conventional design, information mining analysis is usually performed only on a single payment event, and the method for information mining cannot reflect the real business service preference of the user more accurately, so that the accuracy of information pushing is low.

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 processing method and a digital financial service center based on a block chain and cloud computing, in which payment object information in a payment record of a first target resource associated with a payment event is determined, a first evidence storing element list is determined, then a payment record of a second target resource and a corresponding second evidence storing element list are determined, a target evidence storing element matched with the evidence storing element in the first evidence storing element list is selected from the second evidence storing element list, and then target payment object information matched with the payment object information in the payment record of the first target resource in the payment record of the second target resource is determined according to the obtained target evidence storing element list, and then information pushing is performed. Therefore, the situation of iterative transfer of the payment records of the target resources associated with the payment event is considered, so that subsequent information mining analysis can be performed by combining the target payment object information of the iterative transfer, and the accuracy of subsequent information pushing is improved.

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

determining payment object information in a payment record of a first target resource associated with the payment event from a corresponding block chain in a state that any one payment event of the online financial service terminal is activated, and determining a first evidence storage element list capable of representing the payment object information, wherein the payment object information corresponds to a payment object;

determining a payment record of a second target resource from the blockchain, and obtaining a second evidence storing element list capable of representing the payment record of the second target resource, wherein the payment record of the second target resource is the payment record of the target resource associated with the payment record of the first target resource;

matching the second evidence storage element list with the first evidence storage element list, and selecting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the second evidence storage element list based on a matching result to obtain a target evidence storage element list;

determining target payment object information matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource based on the target evidence storing element list, wherein the target payment object information corresponds to a payment object matched with the payment object of the payment object information in the payment record of the first target resource;

and inputting target payment object information corresponding to all activated payment events of the online financial service terminal into a cloud computing service subscribed by the online financial service terminal to generate information to be pushed of the online financial service terminal.

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

determining a payment record mapping feature for the target resource that maps from the payment record for the first target resource to the payment record for the second target resource;

predicting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the payment record of the second target resource based on the payment record mapping characteristic of the target resource to obtain a first predicted target evidence storage element list;

correspondingly, the step of selecting the target evidence-storing element matched with the evidence-storing element in the first evidence-storing element list from the second evidence-storing element list based on the matching result to obtain the target evidence-storing element list includes:

selecting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the second evidence storage element list based on a matching result to obtain a second estimated target evidence storage element list;

and obtaining a target evidence storage element list based on the first estimated target evidence storage element list and the second estimated target evidence storage element list.

In a possible implementation manner of the first aspect, the matching the second list of evidence storing elements with the first list of evidence storing elements, and selecting a target evidence storing element matched with the evidence storing element in the first list of evidence storing elements from the second list of evidence storing elements based on a matching result includes:

determining evidence storing business characteristics between a second evidence storing element in the second evidence storing element list and a first evidence storing element in the first evidence storing element list;

and selecting a target evidence storage element with evidence storage service characteristics meeting preset service rules from the second evidence storage element list.

In a possible implementation manner of the first aspect, the determining, based on the target evidence saving element list, target payment object information that is matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource includes:

determining key evidence storing elements based on a first evidence storing element in the first evidence storing element list and a target evidence storing element corresponding to the first evidence storing element in the target evidence storing element list to obtain a key evidence storing element list;

selecting target key evidence storage elements meeting preset rules from the key evidence storage element list;

and determining target payment object information in the payment record of the second target resource based on the first evidence storing element list and the target key evidence storing element, wherein the target key evidence storing element is positioned at a central node of the target payment object information.

In a possible implementation manner of the first aspect, the step of inputting target payment object information corresponding to all activated payment events of the online financial service terminal into a cloud computing service subscribed by the online financial service terminal and generating information to be pushed by the online financial service terminal includes:

inputting target payment object information corresponding to all activated payment events of the online financial service terminal into a cloud computing service subscribed by the online financial service terminal, and acquiring a pushed target sample obtained by identifying the target payment object information according to an information pushing rule corresponding to the cloud computing service;

obtaining a plurality of keyword analysis results obtained by performing keyword analysis on the pushed target sample, wherein each keyword analysis result consists of a keyword vector of the pushed target sample;

determining a target keyword analysis result meeting a target rule from the plurality of keyword analysis results, and searching a target keyword vector in keyword vectors of the target keyword analysis result in a first push content index library, wherein the first push content index library is used for storing the keyword vectors with push tag information, and the push tag information is used for indicating content tags to which the keyword vectors with the push tag information belong;

determining a target keyword vector with target push tag information in the first push content index library as a push reference node of the push target sample under the condition that the target keyword vector is found in the first push content index library, wherein the push tag information comprises the target push tag information, and the target push tag information is used for indicating a content tag to which the target keyword vector belongs;

determining a target interest point of the push reference node according to the target push tag information, and determining an interest point of the push target sample according to the target interest point of the push reference node;

and generating information to be pushed of the online financial service terminal according to the interest point of the pushed target sample.

In a possible implementation manner of the first aspect, the step of determining, from among the plurality of keyword parsing results, a target keyword parsing result that satisfies a target rule includes:

acquiring carrier characteristic information of information push carriers of all keyword vectors in each keyword analysis result, and determining a first keyword analysis result that the carrier characteristic information of the information push carriers of all the keyword vectors accords with an information push rule corresponding to the cloud computing service, wherein a service characteristic range of the first keyword analysis result is a first service characteristic range;

determining the first keyword analysis result as the target keyword analysis result meeting the target rule under the condition that the first service characteristic range is the characteristic range of a preset service characteristic label;

under the condition that the first service characteristic range is not the characteristic range of a preset service characteristic label, acquiring election information pushing carriers of all keyword vectors in each first keyword analysis result in the first keyword analysis results of the first service characteristic range, and determining a second keyword analysis result with the largest service coverage range of the election information pushing carriers of all keyword vectors, wherein the service characteristic range of the second keyword analysis result is a second service characteristic range, and the second service characteristic range is smaller than or equal to the first service characteristic range;

determining the second keyword analysis result as the target keyword analysis result meeting the target rule under the condition that the second service characteristic range is the characteristic range of a preset service characteristic label;

under the condition that the second service characteristic range is not the characteristic range of a preset service characteristic label, acquiring the variation amplitude of the keyword classification levels of all the keyword vectors in each second keyword analysis result in the second keyword analysis results of the second service characteristic range, and determining a third keyword analysis result with the minimum variation amplitude of the keyword classification levels of all the keyword vectors, wherein the service characteristic range of the third keyword analysis result is a third service characteristic range, and the third service characteristic range is smaller than or equal to the second service characteristic range; determining the third keyword analysis result as the target keyword analysis result meeting the target rule under the condition that the third service characteristic range is the characteristic range of a preset service characteristic label;

under the condition that the third service feature range is not the feature range of a preset service feature label, obtaining the frequent heat of all keyword vectors in each third keyword analysis result in the third keyword analysis result of the third service feature range, and determining a fourth keyword analysis result with the highest frequent heat of all the keyword vectors, wherein the service feature range of the fourth keyword analysis result is a fourth service feature range, the fourth service feature range is smaller than or equal to the third service feature range, and the frequent heat is used for indicating the probability that the keyword vectors and the heat word vectors form a new keyword vector;

and determining the fourth keyword analysis result as the target keyword analysis result meeting the target rule under the condition that the fourth service characteristic range is the characteristic range of a preset service characteristic label.

In a possible implementation manner of the first aspect, the step of generating information to be pushed of the online financial service terminal according to the point of interest of the pushed target sample includes:

obtaining an interest point running service corresponding to an interest point of the pushed target sample, and obtaining a target interest index node list of a reference data source corresponding to a reference pushed content item of the currently selected interest point of the pushed target sample based on the interest point running service;

sequentially acquiring target interest index nodes of the reference data sources for indicating the content characteristics of the subscription information from the target interest index node list of the reference data sources according to the arrangement sequence of the target interest index nodes of the reference data sources in the target interest index node list of the reference data sources;

calling a target interest index node of the reference data source to acquire a first pushing object of a service pushing process of the interest point running service by adopting an index interval of the target interest index node of the reference data source;

marking, in a second push object associated with the first push object in the service push process, a target interest index node of an element reference data source of an interest coverage element carried by an interest point of the obtained push target sample, where the interest coverage element is an interest coverage element matched according to the subscription information content feature;

extracting candidate reference data source units of interest coverage elements in target interest index nodes of the element reference data source, and fitting the candidate reference data source units to generate target reference data source units of the interest coverage elements;

randomly selecting a first content hotspot and a second content hotspot from the reference content hotspots of the interest point operation service, and establishing a hotspot distribution map based on the first content hotspot and the second content hotspot;

acquiring a third content hotspot corresponding to the first content hotspot and a fourth content hotspot corresponding to the second content hotspot in the target reference data source unit;

acquiring hotspot relation data of the first content hotspot and the second content hotspot and the third content hotspot and the fourth content hotspot, and mapping the target reference data source unit to a hotspot distribution map according to the hotspot relation data, wherein the hotspot distribution map is a distribution map established by a reference content hotspot subscribing to information content characteristics in a target interest index node of the reference data source;

acquiring a correlation parameter between the target reference data source unit and the reference content hotspot in the hotspot distribution map, and generating a matching result of a target interest index node of the element reference data source and a target interest index node of the reference data source according to the correlation parameter;

and acquiring business pushing data of a target interest index node aiming at the reference data source based on the matching result in the interest point operation service, thereby generating information to be pushed of the online financial service terminal.

In a possible implementation manner of the first aspect, the obtaining, in the hotspot distribution graph, an association parameter between the target reference data source unit and the reference content hotspot, and generating a matching result between the target interest index node of the element reference data source and the target interest index node of the reference data source according to the association parameter includes:

acquiring content similarity association parameters of corresponding nodes between the target reference data source unit and the reference content hot spots in a hot spot distribution map;

when the content similarity correlation parameter meets similarity standard data, determining that the target interest index node of the element reference data source is successfully matched with the target interest index node of the reference data source; or

Acquiring vector data of a corresponding node between the target reference data source unit and the reference content hotspot in a hotspot distribution map, and acquiring a vector association parameter corresponding to the vector data of the corresponding node;

and when the vector association parameters meet the similarity standard data, determining that the target interest index node of the element reference data source is successfully matched with the target interest index node of the reference data source.

distributing the information to be pushed to the online financial service terminal, acquiring business feedback associated information of a target user of the online financial service terminal aiming at the information to be pushed, and generating a business attribute tendency parameter of the target user;

acquiring attention interaction behavior information and attention confirmation behavior information of the target user according to the service attribute tendency parameters of the target user, wherein the attention interaction behavior information comprises an interaction behavior object and an interaction behavior coverage service, and the attention confirmation behavior information comprises a confirmation behavior object and a confirmation behavior coverage service;

according to the interaction behavior coverage service and the confirmation behavior coverage service, constructing a service hierarchical relationship between the interaction behavior object and the confirmation behavior object, and extracting features based on the interaction behavior object, the confirmation behavior object and the service hierarchical relationship to obtain a predicted interaction behavior feature and a predicted confirmation behavior feature of the target user;

and predicting the behavior object to be confirmed of the target user based on the predicted interaction behavior feature and the predicted confirmation behavior feature, and pushing the behavior object to be confirmed.

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

the system comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining payment object information in a payment record of a first target resource associated with a payment event from a corresponding block chain in a state that any one payment event of the online financial service terminal is activated, and determining a first evidence storing element list capable of representing the payment object information, and the payment object information corresponds to a payment object;

a second determining module, configured to determine a payment record of a second target resource from the blockchain, and obtain a second list of license elements capable of characterizing the payment record of the second target resource, where the payment record of the second target resource is a payment record of the target resource associated with the payment record of the first target resource;

the matching module is used for matching the second evidence storage element list with the first evidence storage element list, and selecting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the second evidence storage element list based on a matching result to obtain a target evidence storage element list;

a third determining module, configured to determine, based on the target evidence storing element list, target payment object information that is matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource, where the target payment object information corresponds to a payment object that is matched with the payment object of the payment object information in the payment record of the first target resource;

the generation module is used for inputting target payment object information corresponding to all activated payment events of the online financial service terminal into a cloud computing service subscribed by the online financial service terminal and generating information to be pushed of the online financial service terminal.

In a third aspect, an embodiment of the present application further provides an information processing system based on a blockchain and cloud computing, where the information processing system based on a blockchain and cloud computing includes a digital financial service center and a plurality of online financial service terminals communicatively connected to the digital financial service center;

the digital financial service center is used for:

In a fourth aspect, an embodiment of the present application further provides a digital financial service center, where the digital financial service 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 online financial 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 information processing method based on blockchain and cloud computing in the first aspect or any one of possible implementations of 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 is caused to execute the method for processing information based on a blockchain and cloud computing in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, the method determines the payment object information in the payment record of the first target resource associated with the payment event, determines the first evidence storing element list capable of representing the payment object information, then determines the payment record of the second target resource, obtains the second evidence storing element list capable of representing the payment record of the second target resource, selects the target evidence storing element matched with the evidence storing element in the first evidence storing element list from the second evidence storing element list, obtains the target evidence storing element list, and then determines the target payment object information matched with the payment object information in the payment record of the first target resource in the payment record of the second target resource, and then carries out information push. Therefore, the situation of iterative transfer of the payment records of the target resources associated with the payment event is considered, so that subsequent information mining analysis can be performed by combining the target payment object information of the iterative transfer, and the accuracy of subsequent information pushing is 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 view of an application scenario of an information processing system based on a block chain and cloud computing according to an embodiment of the present application;

fig. 2 is a schematic flowchart of an information processing method based on a blockchain and cloud computing according to an embodiment of the present disclosure;

fig. 3 is a schematic functional module diagram of an information processing apparatus based on a blockchain and cloud computing according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a digital financial service center for implementing the above information processing method based on a blockchain and cloud computing 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 information processing system 10 based on a blockchain and cloud computing according to an embodiment of the present application. The blockchain and cloud computing based information processing system 10 may include a digital financial service center 100 and an online financial service terminal 200 communicatively connected to the digital financial service center 100. The blockchain and cloud computing based information processing system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the blockchain and cloud computing based information processing 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 online financial services terminal 200 may include 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 digital financial service center 100 and the online financial service terminal 200 in the information processing system 10 based on the blockchain and the cloud computing may cooperatively perform the information processing method based on the blockchain and the cloud computing described in the following method embodiment, and for the specific steps of the digital financial service center 100 and the online 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 solution provided by the present application, the digital financial service center 100 provided by the present application may be applied to scenes such as smart medical, smart city management, smart industrial internet, general service monitoring management, etc. in which a big data technology or a cloud computing technology is applied, and for example, may 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, etc., but not limited thereto.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flowchart of an information processing method based on a block chain and cloud computing according to an embodiment of the present application, and the information processing method based on a block chain and cloud computing according to the present embodiment may be executed by the digital financial service center 100 shown in fig. 1, and the information processing method based on a block chain and cloud computing is described in detail below.

In step S110, in a state where any one payment event of the online financial service terminal 200 is activated, the payment object information in the payment record of the first target resource associated with the payment event is determined from the corresponding blockchain, and a first evidence storage element list capable of representing the payment object information is determined.

For example, the payment object information in the payment record of the first target resource corresponding to the payment identifier included in the payment event may be determined from the corresponding blockchain, and a first list of evidence elements capable of characterizing the payment object information may be determined.

Step S120, determining the payment record of the second target resource from the blockchain, and obtaining a second evidence storing element list capable of representing the payment record of the second target resource.

Wherein the payment record of the second targeted resource is a payment record of the targeted resource associated with the payment record of the first targeted resource. For example, the payment record for the subject resource associated with the payment record for the first subject resource may refer to the payment record for the subject resource for which the same payment event transfer scenario exists.

Step S130, matching the second evidence storage element list with the first evidence storage element list, and selecting a target evidence storage element matched with the evidence storage element in the first evidence storage element list from the second evidence storage element list based on a matching result to obtain a target evidence storage element list.

Step S140, determining target payment object information matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource based on the target evidence storing element list.

The target payment object information corresponds to a payment object matched with the payment object of the payment object information in the payment record of the first target resource.

Step S150, inputting the target payment object information corresponding to all activated payment events of the online financial service terminal 200 into the cloud computing service subscribed by the online financial service terminal 200, and generating information to be pushed of the online financial service terminal 200.

In this embodiment, the target resource may refer to payment plan content related to the execution process of the payment event, for example, payment plan content initiated before payment.

In this embodiment, the payment object information corresponds to a payment object, and the payment object may refer to an entity object initiating a payment transaction in a payment process, for example, an entity object of a certain commodity service, an entity object of a certain consumption node, and the like.

In this embodiment, the evidence storing element may refer to evidence storing information for reflecting characteristic information of the payment object information, such as, but not limited to, location service evidence storing information, time service evidence storing information, transaction object tag evidence storing information, and the like.

In this embodiment, the payment event may refer to an event from an initial initiating node to an ending node when a payment transaction is initiated in a payment process, and the definitions of the initial initiating node and the ending node may be flexibly set, which is not limited herein in detail.

In this embodiment, the cloud computing service subscribed by the online financial service terminal 200 may refer to a service node that is registered in advance by the online financial service terminal 200 and has applied for a relevant cloud computing resource, and the service node may configure a corresponding cloud computing business rule, so that the information to be pushed of the online financial service terminal 200 may be generated in the cloud computing service based on the cloud computing business rule.

Based on the steps, the payment object information in the payment record of the first target resource associated with the payment event is determined, the first evidence storing element list is determined, then the payment record of the second target resource and the corresponding second evidence storing element list are determined, the target evidence storing element matched with the evidence storing element in the first evidence storing element list is selected from the second evidence storing element list, and then the target payment object information matched with the payment object information in the payment record of the first target resource in the payment record of the second target resource is determined according to the obtained target evidence storing element list, and then information pushing is carried out. Therefore, the situation of iterative transfer of the payment records of the target resources associated with the payment event is considered, so that subsequent information mining analysis can be performed by combining the target payment object information of the iterative transfer, and the accuracy of subsequent information pushing is improved.

In one possible implementation, before step S130, the present embodiment may determine a payment record mapping feature of the target resource that maps from the payment record of the first target resource to the payment record of the second target resource. Then, based on the payment record mapping characteristics of the target resource, the target evidence storage element matched with the evidence storage element in the first evidence storage element list is estimated from the payment record of the second target resource, and a first estimated target evidence storage element list is obtained.

Correspondingly, for step S130, a target authentication element matching the authentication element in the first authentication element list may be selected from the second authentication element list based on the matching result to obtain a second pre-estimated target authentication element list, and then, a target authentication element list may be obtained based on the first pre-estimated target authentication element list and the second pre-estimated target authentication element list.

For another example, in another possible implementation manner, for step S130, a certificate storing service characteristic between the second certificate storing element in the second certificate storing element list and the first certificate storing element in the first certificate storing element list may be specifically determined. And then, selecting a target evidence storage element with evidence storage service characteristics meeting preset service rules from the second evidence storage element list.

In a possible implementation manner, for step S140, the embodiment may determine the key evidence storing element based on the first evidence storing element in the first evidence storing element list and the target evidence storing element corresponding to the first evidence storing element in the target evidence storing element list, so as to obtain the key evidence storing element list. On the basis, the target key evidence storing elements meeting the preset rules can be selected from the key evidence storing element list. For example, the predetermined rule may indicate that the feature importance of the evidence element satisfies a predetermined importance.

Therefore, the target payment object information in the payment record of the second target resource can be determined based on the first evidence storing element list and the target key evidence storing element, wherein the target key evidence storing element is located at the central node of the target payment object information. For example, the target payment object information in the payment record of the second target resource may be determined based on the first list of credential elements and the target payment object characterized by the target key credential element.

In one possible implementation, step S150 may be implemented by the following exemplary substeps, which are described in detail below.

In the substep S151, target payment object information corresponding to all activated payment events of the online financial service terminal 200 is input into the cloud computing service subscribed by the online financial service terminal 200, and a pushed target sample obtained by identifying the target payment object information is obtained according to an information pushing rule corresponding to the cloud computing service.

For example, the information push rule corresponding to the cloud computing service may include identification matching rule features of a plurality of push samples, so that a push target sample obtained by identifying the target payment object information may be obtained based on the identification matching rule features of the plurality of push samples.

And a substep S152, obtaining a plurality of keyword analysis results obtained by performing keyword analysis on the pushed target sample.

For example, each keyword parsing result consists of a keyword vector of the push target sample.

And a substep S153, determining a target keyword analysis result meeting the target rule in the various keyword analysis results, and searching a target keyword vector in the keyword vectors of the target keyword analysis result in the first push content index library.

For example, the first push content index repository is configured to store a keyword vector with push tag information, and the push tag information is configured to indicate a content tag to which the keyword vector with push tag information belongs.

In the sub-step S155, when the target keyword vector is found in the first pushed content index library, the target keyword vector having the target pushed tag information in the first pushed content index library is determined as a pushed reference node for pushing the target sample.

For example, the push tag information includes target push tag information indicating a content tag to which the target keyword vector belongs.

And a substep S156, determining a target interest point of the push reference node according to the target push label information, and determining an interest point of the push target sample according to the target interest point of the push reference node.

And a substep S157 of generating the information to be pushed of the online financial service terminal 200 according to the interest point of the pushed target sample.

Based on the substeps, the push reference node for pushing the target sample is provided with the push label information for labeling the content label to which the push reference node belongs, and the interest point of the push target sample is determined, so that the correct identification of the interest point of the push target sample is ensured, and the problem of incomplete coverage of the content label keyword vector due to a full-quantity-based word bank in the related technology is solved, so that the efficiency of identifying the interest point is improved.

In one possible implementation, in the sub-step S153, it can be implemented by the following exemplary embodiments, which are described in detail below.

(1) And acquiring the carrier characteristic information of the information push carriers of all keyword vectors in each keyword analysis result from the various keyword analysis results, and determining that the carrier characteristic information of the information push carriers of all keyword vectors accords with a first keyword analysis result of an information push rule corresponding to the cloud computing service.

For example, the business feature range of the first keyword parsing result is the first business feature range.

(2) And under the condition that the first service characteristic range is the characteristic range of the preset service characteristic label, determining the first keyword analysis result as a target keyword analysis result meeting the target rule.

(3) And under the condition that the first service characteristic range is not the characteristic range of the preset service characteristic label, acquiring election information pushing carriers of all keyword vectors in each first keyword analysis result in the first keyword analysis results of the first service characteristic range, and determining a second keyword analysis result with the largest service coverage range of the election information pushing carriers of all the keyword vectors.

For example, the service feature range of the second keyword parsing result is a second service feature range, and the second service feature range is smaller than or equal to the first service feature range.

(4) And determining the second keyword analysis result as a target keyword analysis result meeting the target rule under the condition that the second service characteristic range is the characteristic range of the preset service characteristic label.

(5) And under the condition that the second service characteristic range is not the characteristic range of the preset service characteristic label, acquiring the variation amplitude of the keyword classification level of all the keyword vectors in each second keyword analysis result in the second keyword analysis results of the second service characteristic range, and determining a third keyword analysis result with the minimum variation amplitude of the keyword classification level of all the keyword vectors.

For example, the service feature range of the third keyword parsing result is a third service feature range, and the third service feature range is smaller than or equal to the second service feature range. And determining the third keyword analysis result as a target keyword analysis result meeting the target rule under the condition that the third service characteristic range is the characteristic range of the preset service characteristic label.

(6) And under the condition that the third service characteristic range is not the characteristic range of the preset service characteristic label, acquiring the frequent heat of all keyword vectors in each third keyword analysis result in the third keyword analysis result of the third service characteristic range, and determining a fourth keyword analysis result with the highest frequent heat of all the keyword vectors.

For example, the service feature range of the fourth keyword analysis result is a fourth service feature range, the fourth service feature range is smaller than or equal to the third service feature range, and the frequent popularity is used to indicate the probability that the keyword vector and the popularity word vector form a new keyword vector.

(7) And determining the fourth keyword analysis result as a target keyword analysis result meeting the target rule under the condition that the fourth service characteristic range is the characteristic range of the preset service characteristic label.

In one possible implementation, in sub-step S157, it may be implemented by the following exemplary embodiments, which are described in detail below.

(1) And obtaining an interest point running service corresponding to the interest point of the pushed target sample, and obtaining a target interest index node list of a reference data source corresponding to a reference pushed content item of the currently selected interest point of the pushed target sample based on the interest point running service.

(2) And sequentially acquiring the target interest index nodes of the reference data sources for indicating the subscription information content characteristics in the target interest index node list of the reference data sources according to the arrangement sequence of the target interest index nodes of the reference data sources in the target interest index node list of the reference data sources.

(3) And calling a target interest index node of the reference data source to acquire a first push object of a service push process of the interest point running service by adopting an index interval of the target interest index node of the reference data source.

(4) And in a service push process, marking an element reference data source target interest index node of an interest coverage element carried by an interest point of the obtained push target sample in a second push object associated with the first push object, wherein the interest coverage element is matched with the interest coverage element according to the content characteristics of the subscription information.

(5) And extracting candidate reference data source units of interest covering elements in the target interest index nodes of the element reference data source, and fitting the candidate reference data source units to generate the target reference data source units of the interest covering elements.

(6) And randomly selecting a first content hotspot and a second content hotspot from the reference content hotspots of the interest point operation service, and establishing a hotspot distribution map based on the first content hotspot and the second content hotspot.

(7) And acquiring a third content hotspot corresponding to the first content hotspot and a fourth content hotspot corresponding to the second content hotspot in the target reference data source unit.

(8) Acquiring hotspot relation data of the first content hotspot and the second content hotspot and the third content hotspot and the fourth content hotspot, and mapping the target reference data source unit to a hotspot distribution map according to the hotspot relation data.

For example, the hotspot distribution graph is a distribution graph established based on a reference content hotspot subscribing to the information content feature in the target interest index node of the reference data source.

(9) And acquiring the association parameters between the target reference data source unit and the reference content hot spots in the hot spot distribution map, and generating a matching result of the target interest index node of the element reference data source and the target interest index node of the reference data source according to the association parameters.

For example, content similarity association parameters of corresponding nodes between the target reference data source unit and the reference content hotspot may be obtained in the hotspot distribution graph. And when the content similarity correlation parameter meets the similarity standard data, determining that the target interest index node of the element reference data source is successfully matched with the target interest index node of the reference data source.

For another example, vector data of a corresponding node between the target reference data source unit and the reference content hotspot is obtained in the hotspot distribution map, and a vector association parameter corresponding to the vector data of the corresponding node is obtained. And when the vector association parameters meet the similar standard data, determining that the target interest index node of the element reference data source is successfully matched with the target interest index node of the reference data source.

(10) And acquiring business push data of the target interest index node aiming at the reference data source based on the matching result in the interest point operation service, thereby generating information to be pushed of the online financial service terminal 200.

Based on the above embodiments (1) - (10), by obtaining and displaying the target interest index node of the reference data source carrying the subscription information content characteristics in the first push object of the service push process of the service running service at the point of interest, the interest overlay element having the same characteristics as the subscription information content can be synchronized and obtained by marking the target interest index node of the element reference data source carrying the interest overlay element with the second push object indicated by the service push process, and finally the target interest index node of the reference data source is adopted to match the target interest index node of the element reference data source, so that the process of automatic matching is realized, and meanwhile, the interaction and matching of the interest overlay element are performed through the subscription information content characteristics, so that the rich interactivity of the content service is effectively improved.

In a possible implementation manner, the information processing method based on the blockchain and the cloud computing provided by this embodiment may further include the following steps, which are described in detail below.

Step S160, distributing the information to be pushed to the online financial service terminal 200, and acquiring the service feedback associated information of the target user of the online financial service terminal 200 for the information to be pushed, and generating the service attribute tendency parameter of the target user.

Step S170, according to the service attribute tendency parameter of the target user, acquiring attention interaction behavior information and attention confirmation behavior information of the target user, wherein the attention interaction behavior information comprises an interaction behavior object and an interaction behavior coverage service, and the attention confirmation behavior information comprises a confirmation behavior object and a confirmation behavior coverage service.

And step S180, constructing a service hierarchical relationship between the interactive behavior object and the confirmation behavior object according to the interactive behavior coverage service and the confirmation behavior coverage service, and extracting features based on the interactive behavior object, the confirmation behavior object and the service hierarchical relationship to obtain the predicted interactive behavior feature and the predicted confirmation behavior feature of the target user.

And step S190, predicting the behavior object to be confirmed of the target user based on the predicted interaction behavior characteristics and the predicted confirmation behavior characteristics, and pushing the behavior object to be confirmed.

Based on the above steps S160 to S190, by considering the service hierarchy relationship of the overlay service of the attention interaction behavior information and the attention confirmation behavior information of the target user and the interaction relationship between the interaction behavior information and the confirmation behavior information, the behavior object to be confirmed that meets the user requirement can be accurately predicted. Therefore, the accuracy of information pushing is improved.

For example, in this embodiment, the service feedback related information may be used to represent service information, such as service operation information, service confirmation information, service rejection information, and the like, fed back by the target user of the online financial service terminal 200 in the process of using the service of the information to be pushed, which has been pushed, and is not limited in this respect. In addition, the service attribute tendency parameter of the target user may be used to characterize service attribute contents that the target user tends to during the service usage process for the information to be pushed, such as attention operation behavior information and attention confirmation behavior information. Therefore, the attention operation behavior information and the attention confirmation behavior information of the target user can be specifically acquired according to the service attribute tendency parameters of the target user.

For example, in the present embodiment, the attention operation behavior information may include a spelling line object and a spelling line overlay service, and the attention confirmation behavior information may include a confirmation behavior object and a confirmation behavior overlay service.

In some possible implementation manners, for example, with respect to step S140, user feature extraction may be performed on the target user to obtain user features of the target user, and then feature fusion is performed on the user features of the target user, the predicted order matching feature vector, and the predicted confirmation behavior features to obtain fusion features, so as to predict the behavior object to be confirmed of the target user according to the fusion features. For example, the behavior object to be confirmed of the target user can be predicted according to the commodity label represented by each feature vector in the fused features.

Based on the above steps, the present embodiment generates the service attribute tendency parameter of the target user by obtaining the service feedback association information of the target user for the information to be pushed of the online financial service terminal 200, thereby further considering the service connectivity graph relationship information of the overlay service of the attention operation behavior information and the attention confirmation behavior information of the target user in the actual service using process, and the mutual influence relationship between the information and the confirmation behavior information by the spelling line based on the service attribute tendency parameter, and can accurately predict the behavior object to be confirmed which meets the user requirement. Therefore, the accuracy of information pushing can be improved compared with the traditional global analysis mode aiming at the service feedback information by accurately predicting the behavior object to be confirmed which meets the user requirements and then further pushing the information.

For example, in one possible implementation manner, for step S160, in the process of acquiring the business feedback related information of the target user of the online financial service terminal 200 for the information to be pushed, and generating the business attribute tendency parameter of the target user, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S161, acquiring the business feedback associated information of the target user of the online financial service terminal 200 for the information to be pushed.

And a substep S162, obtaining a first service feedback attribute and a first service attention feedback parameter of the target user according to the service feedback correlation information.

And a substep S163 of obtaining a predetermined business attribute tendency neural network, wherein the predetermined business attribute tendency neural network is obtained by performing machine learning processing on a third business feedback attribute and a third business attention feedback parameter of sample users, and the sample users comprise business attribute tendency users and non-business attribute tendency users.

And a substep S164, predicting the business attribute tendency parameters of the target user based on the first business feedback attributes, the first business attention feedback parameters and the predetermined business attribute tendency neural network of the target user.

For example, in a possible implementation manner, the first service feedback attribute of the target user may be determined based on the service feedback associated information of the target user for the information to be pushed on the current application platform, and the first service attention feedback parameter of the target user may be determined based on the second service feedback attribute and the second service attention feedback parameter of each user in the user service circle where the target user is located for the information to be pushed, which will be specifically described in the following description.

Based on the foregoing substeps 161-substep S164, by obtaining a first service feedback attribute and a first service attention feedback parameter of the target user, where the first service feedback attribute can represent a variation trend of the target user' S own interest in the corresponding application platform, and the first service attention feedback parameter is determined based on a second service feedback attribute and a second service attention feedback parameter of each user in the user service circle where the target user is located, it can be seen that the first service attention feedback parameter can be used to represent a mutual influence between the target user and other users in the user service circle. Therefore, the service attribute tendency prediction is carried out based on the first service feedback attribute and the first service attention feedback parameter of the target user, the influence of personal factors of the user and the spelling behavior attribute of the application platform on the service attribute tendency behavior of the user is fully considered, and the prediction accuracy can be effectively improved.

For example, in a possible implementation manner, the determination manner of the first service feedback attribute of the target user may include the following implementation manners.

The method comprises the steps of obtaining attention behavior data of each service label of a target user, calculating and obtaining a service attribute parameter of the target user based on the attention behavior data of each service label of the target user and a weight corresponding to each service label, and obtaining a first service feedback attribute of the target user according to the service attribute parameter of the target user and service feedback correlation information matching.

For example, in a possible implementation manner, the determination manner of the first service attention feedback parameter of the target user may include the following implementation manners.

(1) And in the current determination period, selecting a current main service user from the user service circle, wherein the current main service user is a user which is not used as a main service user in the current determination period.

(2) Updating the current second service attention feedback parameters of the current main service user and each associated user thereof based on the current second service feedback attribute of the current main service user for the information to be pushed, and obtaining the updated second service attention feedback parameters of the current main service user and each associated user thereof, wherein the associated user of the current main service user is a user with preset order-sharing behavior with the current main service user on the current application platform.

(3) And returning to the step of selecting the current main service user from all the users in the user service circle until all the users in the user service circle are taken as the past main service users in the current determined period.

(4) And if the preset condition is not met, executing the next determination cycle until the preset condition is met.

(5) And if the preset conditions are met, taking the obtained updated second service attention feedback parameters of the users in the user service circle as the first service attention feedback parameters of the users in the user service circle.

(6) And determining the second service attention feedback parameter of the target user obtained after the iterative computation is completed as the first service attention feedback parameter of the target user.

For example, in one possible implementation manner, for step S180, in the process of performing feature extraction based on the spelling row object, the confirmation behavior object and the business connectivity graph relation information to obtain the predicted spelling row feature vector and the predicted confirmation behavior feature of the target user, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S181 of extracting the characteristics of the spelling single action object according to the relation information of the service connectivity graph to obtain a unit spelling single characteristic vector of the spelling single action object.

And a sub-step S182 of determining a global imposition feature vector for the unit imposition feature vector of the object based on the imposition line. And determining a recent spelling single line as an object in the spelling single line objects according to the spelling single line as the coverage service, and determining a unit spelling single characteristic vector corresponding to the recent spelling single line as the object.

And a substep S183 of fusing the global spelling single feature vector and the unit spelling single feature vector corresponding to the recent spelling single as the object to obtain the predicted spelling single feature vector of the target user.

And a substep S184, extracting the characteristics of the confirmed behavior object according to the relation information of the service connectivity graph to obtain the unit confirmed behavior characteristics of the confirmed behavior object.

And a substep S185, determining a global confirmation behavior characteristic based on the unit confirmation behavior characteristic of the confirmation behavior object, determining a recent confirmation behavior object in the confirmation behavior object according to the confirmation behavior coverage service, and determining the unit confirmation behavior characteristic corresponding to the recent confirmation behavior object.

And a substep S186, fusing the global confirmation behavior characteristics and the unit confirmation behavior characteristics corresponding to the recent confirmation behavior object, and predicting the confirmation behavior characteristics of the target user.

For example, in one possible implementation, and still with respect to step S180, the service connectivity graph relationship information includes a service connectivity graph including a plurality of graph elements and a migration object connecting the two graph elements.

In the process of establishing the service connection graph relation information between the assembly line object and the confirmation behavior object according to the assembly line covering service and the confirmation behavior covering service, the assembly line object and the confirmation behavior object can be used as graph units in a service connection graph, and then a migration object between the graph units in the service connection graph is established for the covering service and the confirmation behavior covering service according to the assembly line.

Wherein, the map unit can comprise a spelling line object element and a confirmation behavior object element.

Therefore, in the process of establishing the migration objects among the map units in the service connection map for the coverage service and the confirmation behavior coverage service according to the spelling single line, the spelling single line can be subjected to coverage service sequence ordering for the coverage service according to the spelling single line to obtain a spelling single line ordering result, then the spelling single line in the service connection map is subjected to pairwise connection for object elements according to the spelling single line ordering result, the confirmation behavior objects are subjected to coverage service sequence ordering according to the confirmation behavior coverage service to obtain a confirmation behavior ordering result.

In this way, the confirmation behavior object elements in the service connection map can be connected pairwise according to the confirmation behavior sequencing result, meanwhile, the nodes in the service connection map are sequentially sequenced for the coverage service and the confirmation behavior coverage service according to the spelling line, after the global sequencing result is obtained, the nodes in the service connection map are connected pairwise according to the global sequencing result, and therefore the service connection map relation information between the spelling line object and the confirmation behavior object can be obtained.

Fig. 3 is a schematic diagram of functional modules of an information processing apparatus 300 based on a block chain and cloud computing according to an embodiment of the present disclosure, and this embodiment may divide the functional modules of the information processing apparatus 300 based on the block chain and cloud computing according to a method embodiment executed by the digital financial service center 100, that is, the following functional modules corresponding to the information processing apparatus 300 based on the block chain and cloud computing may be used to execute each method embodiment executed by the digital financial service center 100. The information processing apparatus 300 based on blockchain and cloud computing may include a first determining module 310, a second determining module 320, a matching module 330, a third determining module 340, and a generating module 350, and the functions of the functional modules of the information processing apparatus 300 based on blockchain and cloud computing are described in detail below.

The first determining module 310 is configured to determine, from the corresponding blockchain, payment object information in a payment record of a first target resource associated with a payment event in a state where any one payment event of the online financial service terminal 200 is activated, and determine a first evidence storing element list capable of representing the payment object information, where the payment object information corresponds to a payment object. The first determining module 310 may be configured to perform the step S110, and for a detailed implementation of the first determining module 310, reference may be made to the detailed description of the step S110.

A second determining module 320, configured to determine a payment record of a second target resource from the blockchain, and obtain a second list of license elements capable of characterizing the payment record of the second target resource, where the payment record of the second target resource is the payment record of the target resource associated with the payment record of the first target resource. The second determining module 320 may be configured to perform the step S120, and as for a detailed implementation of the second determining module 320, reference may be made to the detailed description of the step S120.

And the matching module 330 is configured to match the second evidence storing element list with the first evidence storing element list, and select a target evidence storing element matched with the evidence storing element in the first evidence storing element list from the second evidence storing element list based on a matching result, so as to obtain a target evidence storing element list. The matching module 330 may be configured to perform the step S130, and the detailed implementation of the matching module 330 may refer to the detailed description of the step S130.

The third determining module 340 is configured to determine, based on the target evidence storing element list, target payment object information that is matched with the payment object information of the payment record of the first target resource in the payment record of the second target resource, where the target payment object information corresponds to a payment object that is matched with the payment object of the payment object information in the payment record of the first target resource. The third determining module 340 may be configured to perform the step S140, and for a detailed implementation of the third determining module 340, reference may be made to the detailed description of the step S140.

The generating module 350 is configured to input target payment object information corresponding to all activated payment events of the online financial service terminal 200 into a cloud computing service subscribed by the online financial service terminal 200, and generate information to be pushed of the online financial service terminal 200. The generating module 350 may be configured to perform the step S150, and the detailed implementation of the generating module 350 may refer to the detailed description of the step S150.

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 service subscription operation target, 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 determining 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 determining 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 is a schematic diagram illustrating a hardware structure of the digital financial service center 100 for implementing the control device according to the embodiment of the present disclosure, and as shown in fig. 4, the digital financial service 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 determining module 310, the second determining module 320, the matching module 330, the third determining module 340, and the generating module 350 included in the information processing apparatus 300 based on blockchain and cloud computing shown in fig. 3), so that the processor 110 may execute the information processing method based on blockchain and cloud computing 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 transceive data with the aforementioned online 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 digital financial service center 100, which implement similar principles and technical effects, and this embodiment is not described herein again.

In the embodiment shown in FIG. 4, it should be understood that the Processor may be a global rule superposition matching process (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, an 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 verification processing method based on the blockchain offline payment is implemented as above.

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.

Meanwhile, the present specification uses a specific keyword vector to describe an embodiment of the present specification. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily 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, apparatus, 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, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, or as a stand-alone software package on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or digital financial services terminal. 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 lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, 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 through interactive services, they may also be implemented through software-only solutions, such as installing the described system on an existing digital financial services terminal 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.

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present 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 information processing method based on a block chain and cloud computing is applied to a digital financial service center which is in communication connection with a plurality of online financial service terminals, and the method comprises the following steps:

inputting target payment object information corresponding to all activated payment events of the online financial service terminal into a cloud computing service subscribed by the online financial service terminal to generate information to be pushed of the online financial service terminal;

the step of inputting the target payment object information corresponding to all activated payment events of the online financial service terminal into the cloud computing service subscribed by the online financial service terminal and generating the information to be pushed of the online financial service terminal includes:

2. The block chain and cloud computing based information processing method according to claim 1, wherein the method further comprises:

3. The information processing method based on the blockchain and the cloud computing according to claim 1, wherein the matching the second list of evidence-storing elements with the first list of evidence-storing elements, and selecting a target evidence-storing element matched with the evidence-storing element in the first list of evidence-storing elements from the second list of evidence-storing elements based on a matching result comprises:

4. The information processing method based on blockchain and cloud computing according to claim 1, wherein the determining, based on the target evidence element list, target payment object information in the payment record of the second target resource that matches the payment object information in the payment record of the first target resource includes:

5. The block chain and cloud computing-based information processing method according to claim 1, wherein the step of determining a target keyword resolution result that satisfies a target rule among the plurality of types of keyword resolution results includes:

6. The information processing method based on the blockchain and the cloud computing according to claim 1, wherein the step of generating the information to be pushed of the online financial service terminal according to the interest point of the pushed target sample includes:

7. The information processing method according to claim 6, wherein the obtaining of the association parameter between the target reference data source unit and the reference content hotspot in the hotspot distribution graph and the generating of the matching result between the target interest index node of the element reference data source and the target interest index node of the reference data source according to the association parameter comprise:

8. The information processing method based on blockchain and cloud computing according to any one of claims 1 to 7, wherein the method further comprises:

9. A digital financial service 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 online financial service terminal, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the information processing method based on blockchain and cloud computing according to any one of claims 1 to 8.