CN112069325A

CN112069325A - Big data processing method based on block chain offline payment and cloud service pushing platform

Info

Publication number: CN112069325A
Application number: CN202010902200.1A
Authority: CN
Inventors: 张富平
Original assignee: Individual
Current assignee: Cloud Account Digital Technology Tianjin Co ltd
Priority date: 2020-09-01
Filing date: 2020-09-01
Publication date: 2020-12-11
Anticipated expiration: 2040-09-01
Also published as: CN112069325B; CN112800241A; CN112765365A

Abstract

The embodiment of the application provides a big data processing method based on block chain offline payment and a cloud service push platform, mineable target services of service tags to be mined under target payment environment elements corresponding to an offline bill data set are considered, then mineable target services under all the target payment environment elements are grouped based on a preset subscribed push group, so that the difference between different target payment environment elements and the subscribed push group is considered, and therefore big data mining is performed on knowledge graph data sets of each subscribed push group based on push service graphs corresponding to the subscribed push group, the accuracy of big data mining can be effectively improved, and a big data mining result can be matched with an actual service scene better.

Description

Big data processing method based on block chain offline payment and cloud service pushing platform

Technical Field

The application relates to the technical field of big data, in particular to a big data processing method based on block chain offline payment and a cloud service pushing platform.

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 big data mining still needs to be performed on an offline bill data set of the offline payment scene, so that subsequent service push services can be improved according to analysis results of a big data level.

However, through research by the inventors of the present application, it is found that in the conventional big data mining design, actual payment environment elements (such as payment service scene types, user types of payment users, etc.) are not considered, resulting in low accuracy of big data mining.

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 a big data processing method based on block chain offline payment and a cloud service push platform, in which excavatable target services of a service tag to be excavated under a target payment environment element corresponding to an offline bill data set are considered, and then the excavatable target services under each target payment environment element are grouped based on a predetermined subscribed push group, so that differences between different target payment environment elements and the subscribed push groups are considered, and thus big data mining is performed on each map knowledge data set of the subscribed push group based on a push service image corresponding to the subscribed push group, so that accuracy of big data mining can be effectively improved, and a big data mining result can be better matched with an actual service scene.

In a first aspect, the present application provides a big data processing method based on block chain 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:

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

and acquiring the 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, aiming at each subscribed push group, and 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.

In a possible implementation manner of the first aspect, the step of obtaining the knowledge-graph data that each mineable target service in the set of mineable target services subscribed to the push packet matches the offline billing data set includes:

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.

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

judging whether extended loading service information for indicating that an extensible loading service exists in an extensible target service exists or not in the process of big data mining, and extracting a first knowledge graph of a first extensible target service corresponding to the extended loading service information mined by big data and a second knowledge graph of at least one second extensible target service having an extended loading service relation with the first extensible target service when the extended loading service information is detected;

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

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

fusing the first knowledge graph with knowledge graph nodes corresponding to at least one second knowledge graph according to each identical knowledge graph node to obtain a fused knowledge graph;

adding the first knowledge graph and at least one second knowledge graph to a preset data map classification queue, and establishing a plurality of first data map classification parameters of the first knowledge graph and a plurality of second data map classification parameters of the second knowledge graph based on the data map classification queue;

determining first knowledge expression information of the first excavatable target service according to each first data map classification parameter, determining second knowledge expression information of the 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 graph feature corresponding to the first knowledge expression information and a second knowledge graph feature corresponding to the second knowledge expression information, determining a plurality of knowledge corpus objects corresponding to the fusion knowledge graph 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, and excavating a first knowledge corpus object corresponding to the first knowledge graph feature in the knowledge corpus excavation list in a preset big data excavation process for each knowledge corpus excavation list The corpus portrait characterization feature and a second corpus portrait characterization feature corresponding to the second knowledge graph feature;

and according to 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, splicing the mining results according to a preset priority of knowledge expectation to generate a simulated mining stream, restoring the spliced simulated mining stream, and determining global big data mining information of the first mineable target service and the at least one second mineable target service.

In a possible implementation manner of the first aspect, the step of adding the first knowledge-graph and the at least one second knowledge-graph to a preset data map classification queue includes:

determining expanded mining configuration information of the data map classification queue; the expanded mining configuration information is used for representing a data map classification target distributed when the data map classification queue processes the added knowledge graph, and the data map classification target is used for representing mining characteristic node information when the data map classification queue mines the added knowledge graph;

determining, based on the extended mining configuration information, to add the first knowledge graph to first mined feature node information corresponding to the data map classification queue and to add the second knowledge graph to second mined feature node information corresponding to the data map classification queue;

determining whether an extended loading service exists when the first knowledge-graph and the second knowledge-graph are added to the data map classification queue according to the first mined feature node information and the second mined feature node information; the extended loading service is used for representing the response behavior of extended loading existing in the mining of the data map classification queue;

if not, adjusting the second mining feature node information to obtain third mining feature node information, and adding the first knowledge graph and the second knowledge graph to the data map classification queue based on the first mining feature node information and the third mining feature node information, wherein a feature difference between the third mining feature node information and the second mining feature node information is matched with a feature difference between the first mining feature node information and the second mining feature node information;

and if so, continuously adopting the first mining feature node information and the second mining feature node information to add the first knowledge graph and the second knowledge graph to the data map classification queue.

In a possible implementation manner of the first aspect, the step of establishing a plurality of first data map classification parameters of the first knowledge-graph and a plurality of second data map classification parameters of the second knowledge-graph based on the data map classification queue includes:

determining a first sequence of mining nodes of the first knowledge-graph and a second sequence of mining nodes of the second knowledge-graph based on the data map classification queue; the mining node sequence is used for representing mining business relations of the knowledge graph under different mining nodes;

establishing a plurality of first data map classification parameters of the first knowledge graph and a plurality of second data map classification parameters of the second knowledge graph in the data map classification queue according to the first mining node sequence and the second mining node sequence respectively.

In a possible implementation manner of the first aspect, the determining first knowledge expression information of the first mineable target service according to each first data map classification parameter, and determining second knowledge expression information of the second mineable target service according to each second data map classification parameter includes:

determining a mining node service bitmap corresponding to each first data map classification parameter according to a plurality of mining nodes in each first data map classification parameter and mining portrait map parameters between every two adjacent mining nodes;

determining first knowledge representation information of the first mineable target service based on the mining node traffic bitmap; each digging node in the first data map classification parameters is correspondingly provided with a digging portrait map index parameter, a matching parameter between the digging portrait map index parameter and a digging portrait map index parameter of any digging node is used as a corresponding digging portrait map parameter, and the digging portrait map index parameter is determined according to a digging frequent item mode of the digging node in the first data map classification parameters;

listing the mining node of each second data map classification parameter and the mining portrait map index parameter corresponding to the mining node to obtain a first positioning knowledge intention and a second positioning knowledge intention corresponding to each second data map classification parameter; the first positioning knowledge intention is a positioning knowledge intention corresponding to a mining node of a second data map classification parameter, and the second positioning knowledge intention is a positioning knowledge intention corresponding to a mining portrait map index parameter of the second data map classification parameter;

determining a first intent extraction entity for the first positioning knowledge intent relative to the second positioning knowledge intent and a second intent extraction entity for the second positioning knowledge intent relative to the second positioning knowledge intent;

acquiring at least three target extraction entity nodes with the same extraction entity continuity in the first intention extraction entity and the second intention extraction entity, and determining second knowledge expression information of the second data map classification parameter according to the target extraction entity nodes; wherein the extracted entity continuity is used for characterizing an entity cycle relationship between every two extracted entities.

In a possible implementation manner of the first aspect, the step of summarizing the knowledge corpus objects to obtain at least a plurality of knowledge corpus mining lists of different categories includes:

determining a service tag range of the knowledge graph characteristics corresponding to each knowledge corpus object in the knowledge entity characteristic model;

determining a graph node coincidence range of knowledge graph characteristics corresponding to each knowledge corpus object; the graph node overlapping range is the overlapping part of a first knowledge graph characteristic and a second knowledge graph characteristic in knowledge graph characteristics corresponding to each knowledge corpus object;

determining fusion mining information of a first knowledge graph characteristic and a second knowledge graph characteristic corresponding to each knowledge corpus object; the fusion mining information is obtained by calculating a feature parameter union set of mining directional objects with the first knowledge graph feature and the second knowledge graph feature corresponding to the set service label range;

determining a structured subject feature sequence of each knowledge corpus object according to the service tag range, the map node coincidence range and the fusion mining information of the knowledge map features corresponding to each knowledge corpus object;

and summarizing each knowledge corpus object based on the structured topic feature sequence of each knowledge corpus object to obtain the knowledge corpus mining list of at least a plurality of different categories.

In a possible implementation manner of the first aspect, the step of mining, in a preset big data mining process, a first corpus portrait characterization feature of each corpus object in the corpus mining list, the first corpus portrait characterization feature corresponding to the first knowledge-graph feature, and a second corpus portrait characterization feature corresponding to the second knowledge-graph feature includes:

determining the extended mining configuration information of the structured topic feature sequence corresponding to each knowledge corpus object in each knowledge corpus mining list;

determining data map classification errors of the first knowledge graph characteristic and the second knowledge graph characteristic corresponding to each knowledge corpus object in each summary according to the extended mining configuration information; the data map classification error is used for representing mining error conditions of a first knowledge graph characteristic and a second knowledge graph characteristic corresponding to each knowledge corpus object;

judging whether the difference value of each data map classification error and the reference mining error corresponding to the big data mining process is within a preset difference value interval or not; the preset difference value interval is used for representing the interval where each data map classification error is located when the big data mining process is in normal operation;

when the difference value between each data map classification error and the reference synchronization coefficient corresponding to the big data mining process falls into the preset difference value interval, simulating and verifying a first knowledge graph characteristic and a second knowledge graph characteristic corresponding to each knowledge corpus object in the knowledge corpus mining list based on the big data mining process;

and otherwise, modifying the expanded mining configuration information corresponding to the data map classification error corresponding to the difference value which does not fall into the preset difference value interval according to the parameter updating subprocess of the big data mining process, and returning to the step of determining the data map classification error of the first knowledge graph characteristic and the second knowledge graph characteristic corresponding to each knowledge corpus object in each summary according to the expanded mining configuration information.

In a second aspect, an embodiment of the present application further provides a big data processing apparatus 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 apparatus includes:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an offline bill data set generated by each digital financial service terminal in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set from each digital financial service terminal;

the grouping module is used for acquiring the 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;

and the big data mining module is used for acquiring the 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, aiming at each subscribed push group, and mining the big data of the knowledge graph data set of each subscribed push group based on the push service graph corresponding to the subscribed push group.

In a third aspect, an embodiment of the present application further provides a big data processing system based on blockchain offline payment, where the big data processing system based on blockchain offline payment includes a cloud service push platform and a plurality of digital financial service terminals communicatively connected to the cloud service push platform;

the cloud service pushing platform is used for acquiring an offline bill data set generated by each digital financial service terminal in a block chain offline payment environment and a target payment environment element corresponding to the offline bill data set from each digital financial service terminal;

the cloud service pushing platform is used for acquiring the 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 pushing group, and respectively generating an excavatable target service set of each subscribed pushing group;

the cloud service push platform is used for acquiring, aiming at 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 sketch corresponding to the subscribed push group.

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 big data processing 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 is caused to execute the big data processing 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 method considers the mineable target service of the service tag to be mined under the target payment environment element corresponding to the offline bill data set, and then groups the mineable target service under each target payment environment element based on the predetermined subscribed push group, so that the difference between different target payment environment elements and the subscribed push group is considered, and therefore, the big data mining is performed on the knowledge graph data set of each subscribed push group based on the push service image 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 the actual service scene.

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 view of an application scenario of a big data processing system based on offline payment of a blockchain according to an embodiment of the present disclosure;

fig. 2 is a schematic flowchart of a big data processing method based on block chain offline payment according to an embodiment of the present application;

fig. 3 is a functional block diagram of a big data processing 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 above-described big data processing method based on blockchain offline payment 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 a big data processing system 10 based on offline payment for blockchain according to an embodiment of the present application. The big data processing system 10 based on blockchain offline payment can comprise a cloud service push platform 100 and a digital financial service terminal 200 in communication connection with the cloud service push platform 100. The big data processing system 10 based on offline payment for blockchain shown in fig. 1 is only one possible example, and in other possible embodiments, the big data processing system 10 based on offline payment for blockchain may also include only a part 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 big data processing system 10 based on blockchain offline payment may cooperatively perform the big data processing method based on blockchain offline payment described in the following method embodiment, and for a specific part of the steps performed by 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 a big data processing method based on blockchain offline payment according to an embodiment of the present application, where the big data processing method based on blockchain offline payment according to the present embodiment may be executed by the cloud service push platform 100 shown in fig. 1, and the big data processing method based on blockchain offline payment is described in detail below.

Step S110, obtaining, from each digital financial service terminal 200, an offline bill 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 bill data set.

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 S120, acquiring the 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.

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 S130, aiming at 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 is matched with the offline bill data set, and mining the big data of the knowledge graph data set of each subscribed push group based on the push service graph corresponding to the 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, for example, for step S120, in order to improve the accuracy of the division and reduce redundant information to improve the grouping accuracy, the embodiment may obtain the subscription extension feature sample corresponding to each predetermined subscribed push group, form a subscription extension feature sample sequence of each predetermined subscribed push group, and obtain associated subscription extension feature sample information of each target subscription extension feature sample of each target payment environment element and the subscription extension feature sample of the subscription extension feature sample sequence.

On the basis, the sample service coverage range of the key subscription extended feature sample of each target subscribed push group can be calculated according to the associated subscription extended feature sample information of the target subscription extended feature sample and the subscription extended feature sample of the subscription extended feature sample sequence, and the subscription extended feature sample is selected from the subscription extended feature sample sequence according to the sample service coverage range of the key subscription extended feature sample of each target subscribed push group, so that the initial subscription extended feature sample distribution space is obtained.

In one possible example, if the total material distribution sample traffic coverage of the initial subscription extension feature sample distribution space is greater than the maximum total material distribution sample traffic coverage required for the total material distribution sample traffic coverage, then a first key subscription extension feature sample in the initial subscription extension feature sample distribution space is dispersed to the first distribution sample traffic coverage and a second key subscription extension feature sample in the initial subscription extension feature sample distribution space is aggregated to the first distribution sample traffic coverage.

It should be noted that the second key subscription extended feature sample may be a key subscription extended feature sample whose unit intensity on the subscription map where the key subscription extended feature sample is located is less than a set intensity, the first key subscription extended feature sample may be a key subscription extended feature sample whose unit intensity on the subscription map where the key subscription extended feature sample is located is not less than the set intensity, the service coverage of the first distribution sample may be set according to an actual requirement, but the service coverage of the first sub-sample should not be too different from the service coverage of the maximum total material distribution sample required by the service coverage of the total material distribution sample.

And then, calculating the service coverage range of the total material distribution sample of the initial subscription extended feature sample distribution space after the current adjustment, and if the service coverage range of the total material distribution sample of the initial subscription extended feature sample distribution space after the current adjustment is larger than the maximum service coverage range of the total material distribution sample, executing the above processing on the initial subscription extended feature sample distribution space after the current adjustment again.

For another example, if the total material distribution sample service coverage of the initial subscription extended feature sample distribution space after the current adjustment is smaller than or equal to the maximum total material distribution sample service coverage, the initial subscription extended feature sample distribution space before the current adjustment may be used as a first adjustment distribution space, and the subscribed push groups of the targets are sorted according to the order from low priority to high priority of the subscribed push groups, so as to obtain a target subscribed push group sequence.

On this basis, the mineable target services under each target payment environment element can be grouped according to the target subscribed push grouping sequence, and a mineable target service set of each subscribed push group is generated respectively.

For example, the subscribed push groups may be clustered according to a sequence of subscribed push groups, each cluster including a first subscribed push group and a second subscribed push group associated with a subscription map area of the sequence of subscribed push groups and consistent with a range difference of the subscription map area, the first subscribed push group having a lower priority than the second subscribed push group.

Then, according to the sequence from low priority to high priority of the range difference with the subscription map area, sequentially taking each cluster as a target cluster, and performing the following second adjustment processing on the target clusters: the method further includes increasing a set number of key subscription extension feature samples of a first subscribed push group of a target cluster in the first adjusted distribution space, and decreasing a set number of key subscription extension feature samples of a second subscribed push group of the target cluster in the first adjusted distribution space.

On the basis, whether the service coverage range of the total material distribution sample of the adjusted first adjustment distribution space is larger than the service coverage range requirement of the total material distribution sample can be judged, and if the service coverage range of the total material distribution sample of the adjusted first adjustment distribution space is larger than the service coverage range requirement of the total material distribution sample, the adjusted first adjustment distribution space is used as the final subscription expansion feature sample distribution space. And if the service coverage range of the total material distribution sample of the first adjusted distribution space after the current adjustment is not larger than the service coverage range requirement of the total material distribution sample, taking the next cluster as a new target cluster, and performing second adjustment processing on the new target cluster.

For another example, if the total material distribution sample service coverage of the initial subscription extended feature sample distribution space is smaller than the minimum total material distribution sample service coverage required by the total material distribution sample service coverage, the following third adjustment processing is performed on the initial subscription extended feature sample distribution space: the method further includes increasing a first key subscription extended feature sample in the initial subscription extended feature sample distribution space by a first distribution sample traffic coverage and decreasing a second key subscription extended feature sample in the initial subscription extended feature sample distribution space by the first distribution sample traffic coverage.

On the basis, calculating the service coverage range of the total material distribution sample of the initial subscription extended feature sample distribution space after the current adjustment, and if the service coverage range of the total material distribution sample of the initial subscription extended feature sample distribution space after the current adjustment is smaller than the service coverage range of the minimum total material distribution sample, performing third adjustment processing on the initial subscription extended feature sample distribution space after the current adjustment again. Or, if the total material distribution sample service coverage of the initial subscription extended feature sample distribution space after the current adjustment is greater than or equal to the minimum total material distribution sample service coverage, taking the initial subscription extended feature sample distribution space before the current adjustment as a second adjustment distribution space, and sequencing the subscribed push groups according to the sequence from low priority to high priority of the subscribed push groups to obtain a target subscribed push group sequence.

Thus, the subscribed push groups of the targets can be clustered according to the subscribed push group sequence of the targets, each cluster comprises a first subscribed push group and a second subscribed push group which are associated with the subscription map area of the subscribed push group sequence of the targets and have the same range difference with the subscription map area, and the priority of the first subscribed push group is lower than that of the second subscribed push group.

Then, according to the sequence from low priority to high priority of the range difference with the subscription map area, sequentially taking each cluster as a target cluster, and performing the following fourth adjustment processing on the target clusters: reducing the key subscription extension feature samples of the first subscribed push group of the target cluster in the second adjusted distribution space by a set number, and increasing the key subscription extension feature samples of the second subscribed push group of the target cluster in the second adjusted distribution space by the set number.

Further, this embodiment may determine whether the total material distribution sample service coverage of the second adjusted distribution space after this adjustment is greater than the total material distribution sample service coverage requirement, if the total material distribution sample service coverage of the second adjusted distribution space after this adjustment is greater than the total material distribution sample service coverage requirement, use the second adjusted distribution space after this adjustment as the final subscription extension feature sample distribution space, and if the total material distribution sample service coverage of the second adjusted distribution space after this adjustment is not greater than the total material distribution sample service coverage requirement, use the next cluster as a new target cluster, and perform a fourth adjustment process on the new target cluster.

Therefore, the mineable target services of each subscription extended feature sample in the final subscription extended feature sample distribution space of each target subscribed push group can be classified into the mineable target service set of the subscribed push group respectively.

In one possible implementation, for step S130, in the process of obtaining the knowledge-graph data that each mineable target service in the set of mineable target services subscribed to the push packet matches the offline billing data set, the process may be further implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S131, obtaining a matching keyword vector related to each mineable target service in the mineable target service set subscribed to the push grouping.

Substep S132 matches the corresponding billing plate content from the offline billing data set according to the matching keyword vector associated with each mineable target service.

And a substep S133, determining that each mineable target service in the mineable target service set subscribed to the push packet matches the knowledge-graph data of the offline billing data set according to the knowledge-graph content corresponding to each service record plate in the billing plate content matched with the matching keyword vector related to each mineable target service.

In one possible implementation, for example, for step S130, in the process of performing big data mining on the knowledge-graph data set of each subscribed pushed group based on the pushed service representation corresponding to the subscribed pushed group, the following sub-steps may be performed.

In the substep S134, the push service portrait node parameter 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 S135, 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 substep S136, 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 running configuration information corresponding to the mining unit is the running configuration information which is contained in the push service portrait node and is other than the currently configured running configuration information of the subscribed push group.

And a substep S137, establishing a mining business relation of the mining unit according to the running configuration information corresponding to the mining unit, determining a business matching element of the mining business relation, and obtaining preliminary mining information for performing big data mining on each knowledge graph data set subscribed to the push group by the first mining unit in the business matching element.

And a substep S138, when each mining unit behind the first mining unit is subjected to preliminary mining information screening in sequence according to the hierarchy of the mining unit, screening the mining unit and the preliminary mining information of each mining unit behind the mining unit, reestablishing the mining business relationship of the mining unit according to the screened preliminary mining information, determining the business matching elements of the reestablished mining business relationship, and obtaining the screened preliminary mining information of the mining unit in the business matching elements of the reestablished mining business relationship.

And a substep S139, after the screened preliminary mining information of all mining units is obtained, using the screened preliminary mining information of all mining units as a big data mining result.

In a possible implementation manner, after step S130, the following steps may be further included:

step S140, judging whether extended loading service 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 service 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 service information is detected.

And S150, 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 manner, for step S140, a first knowledge graph of a first mineable target service corresponding to extended loading service information of big data mining and a second knowledge graph of at least one second mineable target service having an extended loading service relationship with the first mineable target service may be extracted from 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, step S150 may be implemented by the following exemplary sub-steps, which are described in detail below.

And a substep S151, fusing the first knowledge graph with knowledge graph nodes corresponding to at least one second knowledge graph according to each identical knowledge graph node to obtain a fused knowledge graph.

The sub-step S152 of adding the first knowledge-map and the at least one second knowledge-map to a preset data map classification queue, and establishing 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 S153, 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 corpus characterization features of each knowledge corpus object in the knowledge corpus excavation list in a preset big data excavation process according to each knowledge corpus excavation list The second corpus of features depicts features.

And a substep S154, 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, performing splicing according to the preset priority of knowledge expectation to generate a simulated mining stream, restoring the simulated mining stream generated by splicing, and determining global big data mining information of the first mineable target service and the at least one second mineable 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.

Exemplarily, in the sub-step S152, the implementation may be realized by the following detailed embodiments, for example, which may be described as follows.

(1) And determining the expanded mining configuration information of the data map classification queue.

In this embodiment, the extended mining configuration information is used to represent a data map classification target allocated when the data map classification queue processes the added knowledge graph, and the data map classification target is used to represent mining feature node information when the data map classification queue mines the added knowledge graph.

(2) And determining first mining feature node information corresponding to the first knowledge graph added to the data map classification queue and second mining feature node information corresponding to the second knowledge graph added to the data map classification queue based on the extended mining configuration information.

(3) Determining whether an extended loading service exists when the first knowledge-graph and the second knowledge-graph are added to the data map classification queue according to the first mined feature node information and the second mined feature node information.

In this embodiment, the extended loading service may be used to characterize a response behavior of extended loading existing in mining of the data map classification queue.

(4) If it is determined that no extended loading service exists when the first knowledge graph and the second knowledge graph are added to the data map classification queue, adjusting the second mining feature node information to obtain third mining feature node information, and adding the first knowledge graph and the second knowledge graph to the data map classification queue based on the first mining feature node information and the third mining feature node information.

In this embodiment, the feature difference between the third mined feature node information and the second mined feature node information is matched with the feature difference between the first mined feature node information and the second mined feature node information.

(5) And if the fact that the expanded loading service exists when the first knowledge graph and the second knowledge graph are added to the data map classification queue is determined, continuously adding the first knowledge graph and the second knowledge graph to the data map classification queue by adopting the first mining feature node information and the second mining feature node information.

In one possible implementation, still in sub-step S152, in the process of establishing the plurality of first data map classification parameters of the first knowledge-graph and the plurality of second data map classification parameters of the second knowledge-graph based on the data map classification queue, the following detailed implementation may be implemented, for example, as described below.

(6) A first sequence of mining nodes of the first knowledge-graph and a second sequence of mining nodes of the second knowledge-graph are determined based on the data map classification queue.

It should be noted that the mining node sequence may be used to represent mining service relationships of the knowledge graph under different mining nodes, such as representing a transitional mining service relationship, a coverage mining service relationship, an increase mining service relationship, and the like, and is not specifically set forth herein.

(7) And establishing a plurality of first data map classification parameters of the first knowledge graph and a plurality of second data map classification parameters of the second knowledge graph in the data map classification queue according to the first mining node sequence and the second mining node sequence respectively.

In a possible implementation manner, for step S153, in order to ensure synchronicity and coherence and facilitate subsequent analysis, the following detailed implementation manner may be implemented, for example, as described below.

(1) Determining a mining node service bitmap corresponding to each first data map classification parameter according to a plurality of mining nodes in each first data map classification parameter and mining portrait map parameters between every two adjacent mining nodes

(2) First knowledge representation information of a first mineable target service is determined based on a mining node traffic bitmap.

And matching parameters between the mining portrait map index parameters and the mining portrait map index parameters of any one mining node are used as corresponding mining portrait map parameters, and the mining portrait map index parameters are determined according to mining frequent item modes of the mining nodes in the first data map classification parameters.

(3) And listing the mining node of each second data map classification parameter and the mining portrait map index parameter corresponding to the mining node to obtain a first positioning knowledge intention and a second positioning knowledge intention corresponding to each second data map classification parameter.

For example, the first positioning knowledge intention may be a positioning knowledge intention corresponding to the mining node of the second data map classification parameter, and the second positioning knowledge intention may be a positioning knowledge intention corresponding to the mining portrait map index parameter of the second data map classification parameter.

(4) A first intent extraction entity of the first localization knowledge intent relative to the second localization knowledge intent and a second intent extraction entity of the second localization knowledge intent relative to the second localization knowledge intent are determined.

(5) And acquiring at least three target extraction entity nodes with the same extraction entity continuity in the first intention extraction entity and the second intention extraction entity, and determining second knowledge expression information of the classification parameters of the second data map according to the target extraction entity nodes.

The extracted entity continuity is used for characterizing entity cycle relationship between every two extracted entities.

In a possible implementation manner, still referring to step S153, in the process of aggregating a plurality of knowledge corpus objects to obtain at least a plurality of knowledge corpus mining lists of different categories, the following detailed implementation manner may be implemented, for example, as described below.

(6) And determining the service label range of the knowledge graph characteristics corresponding to each knowledge corpus object in the knowledge entity characteristic model.

(7) And determining the graph node coincidence range of the knowledge graph characteristics corresponding to each knowledge corpus object.

The graph node overlapping range can be an overlapping portion of a first knowledge graph feature and a second knowledge graph feature in knowledge graph features corresponding to each knowledge corpus object.

(8) And determining fusion mining information of the first knowledge graph characteristic and the second knowledge graph characteristic corresponding to each knowledge corpus object.

The fusion mining information can be obtained by calculating a feature parameter union set of mining directing objects with the service tag ranges set corresponding to the first knowledge graph feature and the second knowledge graph feature.

(9) And determining a structured subject feature sequence (namely a sequence formed by taking the service tag range, the map node coincidence range and the fusion mining information of the knowledge map features as sequences) of each knowledge map object according to the service tag range, the map node coincidence range and the fusion mining information of the knowledge map features corresponding to each knowledge map object.

(10) And summarizing each knowledge corpus object based on the structured topic feature sequence of each knowledge corpus object to obtain at least a plurality of knowledge corpus mining lists of different categories.

For example, the corpus objects with at least one same feature parameter in each feature parameter in the structured topic feature sequence may be collected into a corpus mining list of a category corresponding to the same feature parameter, so as to obtain at least a plurality of corpus mining lists of different categories.

In a possible implementation manner, still referring to step S153, in the process of mining the first and second knowledge-graph features corresponding to each corpus object in the corpus mining list in the preset big data mining process, the following detailed implementation manner may be implemented, for example, as described below.

(11) And determining the extended mining configuration information of the structured topic feature sequence corresponding to each knowledge corpus object in each knowledge corpus mining list.

(12) And determining data map classification errors of the first knowledge graph characteristic and the second knowledge graph characteristic corresponding to each knowledge corpus object in each summary according to the expanded mining configuration information.

The data map classification error can be used for representing mining error conditions of the first knowledge graph feature and the second knowledge graph feature corresponding to each knowledge corpus object.

(13) And judging whether the difference value of each data map classification error and the reference mining error corresponding to the big data mining process is within a preset difference value interval.

The preset difference value interval can be used for representing the interval where each data map classification error is located when the big data mining process is in normal operation.

(13) When the difference value between each data map classification error and the reference synchronization coefficient corresponding to the big data mining process falls into a preset difference value interval, the first knowledge graph characteristic and the second knowledge graph characteristic corresponding to each knowledge corpus object in the knowledge corpus mining list can be simulated and verified based on the big data mining process.

(14) Otherwise, when the difference between each data map classification error and the reference synchronization coefficient corresponding to the big data mining process does not fall into the preset difference interval, modifying the expanded mining configuration information corresponding to the data map classification error corresponding to the difference not falling into the preset difference interval according to the parameter updating subprocess of the big data mining process, and returning to the step of determining the data map classification errors of the first knowledge graph feature and the second knowledge graph feature corresponding to each corpus object in each summary according to the expanded mining configuration information.

In one possible implementation, for example, in the process of restoring the spliced analog 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 analog mining stream may be reversely converted according to each corresponding analog 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 in step S154.

Fig. 3 is a schematic functional module diagram of a big data processing apparatus 300 based on blockchain offline payment according to an embodiment of the present disclosure, in this embodiment, functional modules of the big data processing apparatus 300 based on blockchain offline payment may be divided according to a method embodiment executed by the cloud service push platform 100, that is, the following functional modules corresponding to the big data processing apparatus 300 based on blockchain offline payment may be used to execute each method embodiment executed by the cloud service push platform 100. The device 300 for processing big data based on blockchain offline payment may include an obtaining module 310, a grouping module 320, and a big data mining module 330, and the functions of the functional modules of the device 300 for processing big data based on blockchain offline payment are described in detail below.

The obtaining module 310 is configured to obtain, from each digital financial service terminal 200, an offline billing data set generated by each digital financial service terminal 200 in a blockchain offline payment environment and a target payment environment element corresponding to the offline billing data set. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.

The grouping module 320 is configured to obtain the mineable target services of the service tag to be mined under the target payment environment element, group the mineable target services under each target payment environment element according to a predetermined subscribed push group, and generate a mineable target service set of each subscribed push group respectively. The grouping module 320 may be configured to perform the step S120, and the detailed implementation of the grouping module 320 may refer to the detailed description of the step S120.

And the big data mining module 330 is configured to, for each subscribed push group, obtain the knowledge graph data that each mineable target service in the mineable target service set of the subscribed push group matches the offline billing data set, and perform big data mining on the knowledge graph data set of each subscribed push group based on the push service sketch corresponding to the subscribed push group. The big data mining module 330 may be configured to perform the step S130, and the detailed implementation of the big data mining 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 obtaining 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 obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

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 a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the grouping module 320, and the big data mining module 330 included in the big data processing apparatus 300 for offline payment based on blockchain shown in fig. 3), so that the processor 110 may execute the big data processing method for offline payment based on blockchain 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 big data processing method based on the block chain 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. A big data processing 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:

2. The big data processing method for offline payment based on blockchain according to claim 1, wherein said step of obtaining knowledge-graph data of each mineable target service in the mineable target service set of subscribed push packets matching the offline billing data set comprises:

3. The big data processing method based on blockchain offline payment according to claim 1 or 2, wherein the method further comprises:

4. The big data processing method based on blockchain offline payment according to claim 1, wherein the step of determining global big data mining information between the first knowledge-graph and at least one second knowledge-graph according to a preset artificial intelligence model comprises:

5. The big data processing method based on offline blockchain payment according to claim 4, wherein the step of adding the first knowledge graph and the at least one second knowledge graph to a preset data map classification queue comprises:

6. The method of claim 4, wherein the step of establishing a plurality of first data map classification parameters of the first knowledge-graph and a plurality of second data map classification parameters of the second knowledge-graph based on the data map classification queue comprises:

7. The method of claim 4, wherein the step of determining the first knowledge representation information of the first mineable target service according to each first data map classification parameter and determining the second knowledge representation information of the second mineable target service according to each second data map classification parameter comprises:

8. The method of claim 7, wherein the step of aggregating the plurality of corpus objects to obtain at least a plurality of corpus mining lists of different categories comprises:

9. The big data processing method based on offline blockchain payment according to claim 8, wherein said step of mining a first corpus sketch feature corresponding to the first knowledge graph feature and a second corpus sketch feature corresponding to the second knowledge graph feature of each corpus object in the corpus mining list in a preset big data mining process comprises:

10. A cloud service push platform, characterized in that the cloud service push platform comprises a processor, a machine-readable storage medium, and a network interface, 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 with at least one digital financial service terminal, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the big data processing method based on block chain offline payment according to any one of claims 1 to 9.