CN113051395A

CN113051395A - Keyword clustering method and system based on cloud computing and big data

Info

Publication number: CN113051395A
Application number: CN202110241444.4A
Authority: CN
Inventors: 卢霞浩
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-09-15
Filing date: 2020-09-15
Publication date: 2021-06-29
Also published as: CN112187890A; CN113051346A; CN112187890B

Abstract

The embodiment of the application provides a keyword clustering method and system based on cloud computing and big data, and the hot spot information tracking is carried out on the keyword clustering information of the service big data record information to obtain a hot spot information characteristic label set corresponding to the service big data record information, so that the target hot spot information can be self-adaptive to the service big data record information, namely the target hot spot information is more attached to the service big data record information, and the matching degree of information distribution is improved.

Description

Keyword clustering method and system based on cloud computing and big data

Technical Field

The application relates to the technical field of cloud computing and big data, in particular to a keyword clustering method and system based on cloud computing and big data.

Background

With the rapid development of cloud computing and big data technology, the application range of the cloud computing and big data technology is wider and wider, and the big data business information can be analyzed by applying the strong cloud computing capability of the cloud block chain financial cloud center, so that the intention development rules of a large number of users are recognized, and the follow-up business service updating and product technology research and development are facilitated.

However, the hot spot information extracted by the related technology has poor adaptivity, so that the generated hot spot information does not conform to the actual hot spot distribution condition enough, and the matching degree of information distribution is not ideal.

Disclosure of Invention

In order to overcome at least the above-mentioned deficiencies in the prior art, the present application aims to provide a method and a system for clustering keywords based on cloud computing and big data, wherein hot spot information tracking is performed on keyword clustering information of service big data record information to obtain a target hot spot information set corresponding to the service big data record information, so that the target hot spot information can be adaptive to the service big data record information, that is, the target hot spot information is more fit with the service big data record information, and further, according to a hot spot information feature tag set, the classified service big data record information and the target hot spot information set are fused to obtain a hot spot information association map including the target hot spot information, so that the generated hot spot information association map is more suitable for an actual hot spot distribution situation, thereby generating information hot spot information distributed to a plurality of digital financial terminals, thereby improving the matching degree of information distribution.

In a first aspect, the present application provides a keyword clustering method based on cloud computing and big data, which is applied to a blockchain financial cloud center, where the blockchain financial cloud center is in communication connection with a plurality of digital financial terminals, and the method includes:

classifying the service big data record information according to an information distribution rule and a distributed cloud computing task to obtain classified service big data record information, and performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information;

performing hotspot information tracking processing on the keyword clustering information of the service big data record information to obtain a target hotspot information set corresponding to the service big data record information, and performing feature tag tracking processing on the keyword clustering information of the service big data record information to obtain a hotspot information feature tag set corresponding to the service big data record information;

according to the hotspot information feature tag set, carrying out fusion processing on the classified service big data record information and the target hotspot information set to obtain a hotspot information association map comprising target hotspot information;

and generating corresponding information hotspot information distributed to the plurality of digital financial terminals according to the hotspot information association map comprising the target hotspot information.

In one possible implementation manner of the first aspect, the keyword clustering script for keyword clustering includes a keyword clustering program and a tracking program;

the step of performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information comprises the following steps:

clustering the classified service big data record information based on keywords through the keyword clustering program to obtain keyword clustering information of the service big data record information;

the hot spot information tracking processing is carried out on the keyword clustering information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information, and the method comprises the following steps:

tracking the keyword clustering information of the service big data record information based on a hot spot information label through the tracking program to obtain a target hot spot information set corresponding to the service big data record information;

the step of performing feature tag tracking processing on the keyword clustering information of the service big data record information to obtain a hotspot information feature tag set corresponding to the service big data record information comprises the following steps:

and tracking the keyword clustering information of the service big data record information based on a characteristic label space through the tracking program to obtain a hotspot information characteristic label set corresponding to the service big data record information.

In a possible implementation manner of the first aspect, the tracing program includes a plurality of tracing nodes having a tracing association relationship with each other;

the tracking processing based on the hot spot information tag is carried out on the keyword clustering information of the service big data record information through the tracking program to obtain a target hot spot information set corresponding to the service big data record information, and the method comprises the following steps:

performing service characteristic tracking on the keyword clustering information of the service big data record information through a first tracking node in the plurality of tracking nodes which have tracking association relation with each other;

and outputting the tracking result of the first tracking node to a subsequent tracking node with a tracking association relation therebetween, continuing to perform service feature tracking and tracking result output in the subsequent tracking node with the tracking association relation therebetween until the tracking node outputs the service feature tracking and tracking result to the last tracking node, mapping the tracking result output by the last tracking node to a target hotspot information tag, and taking the mapping result as a target hotspot information set corresponding to the service big data record information.

the tracking processing based on the feature tag space is performed on the keyword clustering information of the service big data record information through the tracking program to obtain a hotspot information feature tag set corresponding to the service big data record information, and the method comprises the following steps:

and outputting the tracking result of the first tracking node to a subsequent tracking node with a tracking association relation therebetween, continuing to perform service feature tracking and tracking result output in the subsequent tracking node with the tracking association relation therebetween until the tracking node outputs the service feature tracking and tracking result to the last tracking node, mapping the tracking result output by the last tracking node to a feature tag space, and taking the mapping result as a hot spot information feature tag set corresponding to the service big data record information.

In a possible implementation manner of the first aspect, the keyword clustering program includes a plurality of clustering nodes having a tracking association relationship with each other;

the clustering based on keywords is carried out on the classified service big data record information through the keyword clustering program to obtain the keyword clustering information of the service big data record information, and the method comprises the following steps:

clustering the classified service big data record information through a first clustering node of the clustering nodes with tracking incidence relation;

clustering the first clustering node and outputting the clustering result to the subsequent clustering nodes with tracking association relationship between each other, so as to continue clustering and outputting the clustering result in the subsequent clustering nodes with tracking association relationship between each other until the clustering result is output to the last clustering node;

and taking the clustering result output by the last clustering node as the keyword clustering information of the service big data record information.

In a possible implementation manner of the first aspect, when the tracing program includes a plurality of tracing nodes having a tracing association relationship with each other, and when a cross-node association relationship exists between the tracing node and the clustering node at the same level, the tracing program performs a tracing process based on a hot spot information tag on the keyword clustering information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information, including:

by a first one of the plurality of tracking nodes having a tracking relationship with each other, performing service characteristic tracking on the keyword clustering information of the service big data record information, fusing a tracking result with a clustering result output by a clustering node of the cross-node incidence relation of the first tracking node, taking the fused result as the tracking result of the first tracking node, and outputting the tracking result to a subsequent tracking node with a tracking incidence relation, continuously performing service feature tracking, fusion processing and tracking result output in the subsequent tracking nodes with tracking association relation between each other until the tracking nodes output to the last tracking node, mapping the tracking result output by the last tracking node to a target hotspot information tag, taking the mapping result as a target hotspot information set corresponding to the service big data record information;

when the tracing program includes a plurality of tracing nodes having a tracing association relationship with each other and a cross-node association relationship exists between the tracing nodes of the same level and the clustering nodes, the tracing program performs a feature tag space-based tracing process on the keyword clustering information of the service big data record information to obtain a hot spot information feature tag set corresponding to the service big data record information, including:

by a first one of the plurality of tracking nodes having a tracking relationship with each other, performing service characteristic tracking on the keyword clustering information of the service big data record information, fusing a tracking result with a clustering result output by a clustering node of the cross-node incidence relation of the first tracking node, taking the fused result as the tracking result of the first tracking node, and outputting the tracking result to a subsequent tracking node with a tracking incidence relation between each other, continuing to perform service feature tracking, fusion processing and tracking result output in the subsequent tracking nodes with tracking association relation between each other until the tracking nodes output to the last tracking node, mapping the tracking result output by the last tracking node to a feature tag space, and using the mapping result as a hotspot information characteristic label set corresponding to the service big data record information.

In a possible implementation manner of the first aspect, the fusing the classified service big data record information and the target hotspot information set according to the hotspot information feature tag set to obtain a hotspot information association map including target hotspot information includes:

executing the following processing for each hotspot information feature tag in the set of hotspot information feature tags:

fusing the tag characteristic value corresponding to the hot spot information characteristic tag in the classified service big data record information with the tag characteristic value of the hot spot information characteristic tag in the hot spot information characteristic tag set to obtain a first tag characteristic value of the hot spot information characteristic tag;

weighting the tag characteristic values of the hot spot information characteristic tags in the hot spot information characteristic tag set, and fusing the weighting processing result with the tag characteristic values of the hot spot information characteristic tags corresponding to the target hot spot information set to obtain second tag characteristic values of the hot spot information characteristic tags;

weighting the first tag characteristic value and the second tag characteristic value to obtain the characteristic of the hotspot information characteristic tag;

according to the characteristics of the hotspot information characteristic labels, matching corresponding hotspot title characteristics from the classified service big data record information, and matching corresponding hotspot content characteristics from the target hotspot information set;

fusing the matched hot spot title characteristics and the matched hot spot content characteristics to obtain a hot spot map node;

and splicing all the hot spot map nodes according to the hot spot service relationship to obtain a hot spot information association map comprising target hot spot information.

In a possible implementation manner of the first aspect, the step of generating, according to the hotspot information association map including the target hotspot information, information hotspot information distributed to the plurality of digital financial terminals includes:

extracting a hot spot map node unit corresponding to each target hot spot information in the hot spot information association map, and extracting hot spot label feature vectors of the hot spot map node units in parallel while acquiring an original information hot spot service list associated with the hot spot map node units in pushing from a map data source of the hot spot map node units;

determining screening rule information for screening the original information hotspot service list based on the extracted hotspot tag feature vector, extracting rule matching parameters of a plurality of screening rule nodes to be used and service association information among different screening rule nodes from the screening rule information, and screening the plurality of screening rule nodes to be used according to the rule matching parameters and the service association information to obtain at least two target screening rule elements; the coverage characteristic range of the rule matching parameters of the target screening rule elements is located in a set characteristic range, and the difference degree of the service association information between different target screening rule elements is smaller than a set value;

screening the original information hotspot service list through the target screening rule element to obtain an information hotspot service list to be pushed;

determining hotspot tag compatible distribution of the information hotspot service list to be pushed according to a target hotspot tag feature vector determined from a preset subscription hotspot record, and determining hotspot tag expansion distribution of the information hotspot service list to be pushed according to the determined service tags in the information hotspot service list to be pushed;

and extracting key information hotspot information from the information hotspot service list to be pushed based on the hotspot tag compatible distribution and the hotspot tag extended distribution to obtain a key information hotspot information set, and respectively distributing the key information hotspot information set to the plurality of digital financial terminals.

In a possible implementation manner of the first aspect, the step of extracting key information hot spot information from the to-be-pushed information hot spot service list based on the hot spot tag compatibility distribution and the hot spot tag expansion distribution to obtain a key information hot spot information set includes:

performing service distribution on the information hotspot service list to be pushed based on the hotspot tag expansion distribution to obtain a plurality of distribution service objects, and calculating the distribution service influence of each distribution service object according to the incidence relation between each distribution service object and other distribution service objects;

sorting the shunting service objects according to the descending order of the influence of the shunting service to obtain a shunting service object sorting set;

performing key information hot spot information extraction on each split service object in the split service object sorting set in sequence based on the hot spot label compatible distribution, and calculating the current hot spot influence parameters and the current compatible distribution parameters of a group of key information hot spot information when each group of key information hot spot information is extracted;

when the current hotspot influence parameters and the current compatible distribution parameters meet set conditions, key information hotspot information extraction is continuously carried out according to the distribution service object sorting set;

judging whether the current hotspot influence parameter and the current compatible distribution parameter meet set conditions, deleting the current set of key information hotspot information and returning to traverse when the current hotspot influence parameter and the current compatible distribution parameter do not meet the set conditions, and extracting the key information hotspot information of the distribution service objects of the next sequencing sequence corresponding to the current set of key information hotspot information until the extraction of the key information hotspot information of all the distribution service objects in the distribution service object sequencing set is completed;

the step of judging whether the current hotspot influence parameter and the current compatible distribution parameter meet the set conditions specifically includes the following steps:

determining a first subscription frequency of a current hotspot influence parameter and a second subscription frequency of a current compatible distribution parameter according to the distribution coverage service of the distribution service object sorting set;

comparing the first frequency of subscription with the second frequency of subscription;

when the first subscription frequency is greater than the second subscription frequency, judging whether the current hotspot influence parameter exceeds a first preset value; when the current hotspot influence parameter does not exceed the first preset value, judging whether the current compatible distribution parameter is lower than a second preset value, and when the current compatible distribution parameter is lower than the second preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter meet set conditions; when the current compatible distribution parameter is greater than or equal to the second preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet a set condition; when the current hotspot influence parameter exceeds the first preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet set conditions; the first preset value and the second preset value are determined according to a first mapping value of a difference value of the first subscription frequency and the second subscription frequency in a first preset mapping list;

when the first subscription frequency is less than or equal to the second subscription frequency, judging whether the current hotspot influence parameter exceeds a third preset value; when the current hotspot influence parameter does not exceed the third preset value, judging whether the current compatible distribution parameter is lower than a fourth preset value, and when the current compatible distribution parameter is lower than the fourth preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter meet set conditions; when the current compatible distribution parameter is greater than or equal to the fourth preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet a set condition; when the current hotspot influence parameter exceeds the third preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet a set condition; the third preset value and the fourth preset value are determined according to second mapping values of the first subscription frequency and the second subscription frequency in a second preset mapping list respectively, and the first preset mapping list and the second preset mapping list are complementary lists.

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

the classification processing module is used for classifying the service big data record information according to the information distribution rule and the distributed cloud computing task to obtain classified service big data record information, and performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information;

the tracking processing module is used for tracking hotspot information of the keyword clustering information of the service big data record information to obtain a target hotspot information set corresponding to the service big data record information, and tracking the characteristic tag of the keyword clustering information of the service big data record information to obtain a hotspot information characteristic tag set corresponding to the service big data record information;

the fusion processing module is used for performing fusion processing on the classified service big data record information and the target hotspot information set according to the hotspot information feature tag set to obtain a hotspot information association map comprising target hotspot information;

and the distribution module is used for generating and distributing corresponding information hotspot information to the plurality of digital financial terminals according to the hotspot information association map comprising the target hotspot information.

In a third aspect, an embodiment of the present application further provides a cloud computing and big data based keyword clustering system, where the cloud computing and big data based keyword clustering system includes a blockchain financial cloud center and a plurality of digital financial terminals communicatively connected to the blockchain financial cloud center;

the block chain financial cloud center is used for classifying the service big data record information according to the information distribution rule and the distributed cloud computing task to obtain classified service big data record information, and performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information;

the block chain financial cloud center is used for tracking hot spot information of the keyword clustering information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information, and tracking feature tags of the keyword clustering information of the service big data record information to obtain a hot spot information feature tag set corresponding to the service big data record information;

the block chain financial cloud center is used for fusing the classified service big data record information and the target hotspot information set according to the hotspot information feature tag set to obtain a hotspot information association map comprising target hotspot information;

the block chain financial cloud center is used for generating and distributing corresponding information hotspot information to the plurality of digital financial terminals according to the hotspot information association map comprising the target hotspot information.

In a fourth aspect, an embodiment of the present application further provides a blockchain financial cloud center, where the blockchain financial cloud 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 in communication connection with at least one digital financial 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 cloud computing and big data based keyword clustering method 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 executes the method for clustering keywords based on cloud computing and big data in the first aspect or any one of the possible implementations of the first aspect.

Based on any one of the above aspects, the hot spot information tracking is performed on the keyword clustering information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information, so that the target hot spot information can be adaptive to the service big data record information, that is, the target hot spot information fits the service big data record information better, and further, the classified service big data record information and the target hot spot information set are fused according to the hot spot information feature tag set, so that a hot spot information association map including the target hot spot information can be obtained, the generated hot spot information association map better conforms to the actual hot spot distribution condition, and corresponding information hot spot information distributed to a plurality of digital financial terminals is generated, and the matching degree of information distribution 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 a keyword clustering system based on cloud computing and big data according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a keyword clustering method based on cloud computing and big data according to an embodiment of the present application;

fig. 3 is a schematic functional module diagram of a keyword clustering apparatus based on cloud computing and big data according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a blockchain financial cloud center for implementing the cloud computing and big data-based keyword clustering method according to the embodiment of the present application.

Detailed Description

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

Fig. 1 is an interaction diagram of a keyword clustering system 10 based on cloud computing and big data according to an embodiment of the present application. The cloud computing and big data based keyword clustering system 10 may include a blockchain financial cloud center 100 and a digital financial terminal 200 communicatively connected to the blockchain financial cloud center 100. The cloud computing and big data based keyword clustering system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the cloud computing and big data based keyword clustering system 10 may also include only some of the components shown in fig. 1 or may also include other components.

In this embodiment, the digital financial 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 blockchain financial cloud center 100 and the digital financial terminal 200 in the cloud computing and big data based keyword clustering system 10 may cooperatively perform the cloud computing and big data based keyword clustering method described in the following method embodiment, and for a specific part of the steps performed by the blockchain financial cloud center 100 and the digital financial 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 blockchain financial cloud 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 may be applied, and for example, may also be applied to scenes including 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, blockchain 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 a keyword clustering method based on cloud computing and big data according to an embodiment of the present application, where the keyword clustering method based on cloud computing and big data according to the present embodiment may be executed by the blockchain financial cloud center 100 shown in fig. 1, and the keyword clustering method based on cloud computing and big data is described in detail below.

Step S110, classifying the service big data record information according to the information distribution rule and the distributed cloud computing task to obtain classified service big data record information, and performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information.

Step S120, performing hotspot information tracking processing on the keyword clustering information of the service big data record information to obtain a target hotspot information set corresponding to the service big data record information, and performing feature tag tracking processing on the keyword clustering information of the service big data record information to obtain a hotspot information feature tag set corresponding to the service big data record information.

And step S130, according to the hot spot information feature tag set, performing fusion processing on the classified service big data record information and the target hot spot information set to obtain a hot spot information association map comprising target hot spot information.

Step S140, generating and distributing corresponding information hotspot information to the plurality of digital financial terminals 200 according to the hotspot information association map including the target hotspot information.

In this embodiment, the information distribution rule may include an information distribution collection item subscribed by the service provider in advance, where the information distribution collection item may refer to a service type tag referred to when performing subsequent information distribution, and thus, the corresponding service big data record information may be collected based on the service type tag. The cloud computing task may include a task node for performing classification processing on the service big data record information, such as a trigger time node, or a trigger service node, and is not limited in particular herein. Therefore, the service big data record information can be classified to obtain classified service big data record information, and the specific classification processing mode can be used for classifying and analyzing the service big data record information based on a pre-configured classification principle.

Based on the design, in the embodiment, the hot spot information tracking is performed on the keyword clustering information of the service big data record information to obtain the target hot spot information set corresponding to the service big data record information, so that the target hot spot information can be adaptive to the service big data record information, that is, the target hot spot information fits the service big data record information better, and further, according to the hot spot information feature tag set, the classified service big data record information and the target hot spot information set are fused to obtain the hot spot information association map including the target hot spot information, so that the generated hot spot information association map better conforms to the actual hot spot distribution condition, and corresponding information hot spot information distributed to a plurality of digital financial terminals is generated, and the matching degree of information distribution is improved.

In a possible implementation manner, in the process of performing keyword clustering, a keyword clustering script for keyword clustering may be configured in advance, and specifically, the keyword clustering script may include a keyword clustering program and a tracking program.

Based on this, in step S110, in the process of performing keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information, the keyword clustering program may perform keyword-based clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information.

Further, in step S120, in the process of performing hot spot information tracking processing on the keyword cluster information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information, the tracking program may perform hot spot information tag-based tracking processing on the keyword cluster information of the service big data record information to obtain a target hot spot information set corresponding to the service big data record information. And in the process of tracking the characteristic tag of the keyword cluster information of the service big data record information to obtain the hotspot information characteristic tag set corresponding to the service big data record information, tracking the keyword cluster information of the service big data record information based on the characteristic tag space through a tracking program to obtain the hotspot information characteristic tag set corresponding to the service big data record information.

The above scheme is described in detail below with reference to several possible examples.

Example a:

the tracing program comprises a plurality of tracing nodes which have tracing association relation with each other.

In the process of tracking the keyword clustering information of the service big data record information based on the hotspot information label through the tracking program to obtain the target hotspot information set corresponding to the service big data record information, the service characteristic tracking can be carried out on the keyword clustering information of the service big data record information through a first tracking node in a plurality of tracking nodes with tracking incidence relation. And then, outputting the tracking result of the first tracking node to the subsequent tracking nodes with tracking association relation between each other, continuing to perform service characteristic tracking and tracking result output in the subsequent tracking nodes with tracking association relation between each other until the tracking nodes output the last tracking node, mapping the tracking result output by the last tracking node to a target hotspot information label, and taking the mapping result as a target hotspot information set corresponding to the service big data record information.

Example B:

In the process of tracking the keyword clustering information of the service big data record information based on the characteristic tag space through the tracking program to obtain the hotspot information characteristic tag set corresponding to the service big data record information, the service characteristic tracking can be carried out on the keyword clustering information of the service big data record information through a first tracking node in a plurality of tracking nodes with tracking incidence relation. And then, outputting the tracking result of the first tracking node to the subsequent tracking nodes with tracking association relation between each other, continuing to perform service characteristic tracking and tracking result output in the subsequent tracking nodes with tracking association relation between each other until the tracking result is output to the last tracking node, mapping the tracking result output by the last tracking node to a characteristic label space, and taking the mapping result as a hot spot information characteristic label set corresponding to the service big data record information.

Example C:

the keyword clustering program comprises a plurality of clustering nodes which have tracking association relation with each other.

In the process of clustering the classified service big data record information based on the keywords through the keyword clustering program to obtain the keyword clustering information of the service big data record information, clustering the classified service big data record information through a first clustering node of a plurality of clustering nodes with tracking incidence relation. Then, clustering of the first clustering node is output to the subsequent clustering nodes with tracking association relationship, clustering and clustering result output are continued in the subsequent clustering nodes with tracking association relationship until the last clustering node is output, and the clustering result output by the last clustering node is used as the keyword clustering information of the service big data recording information.

In a possible implementation manner, when the tracking program includes a plurality of tracking nodes having tracking association relations with each other, and a cross-node association relation exists between a tracking node and a cluster node of the same hierarchy, in the process of performing tracking processing based on a hot spot information tag on keyword cluster information of service big data record information through the tracking program to obtain a target hot spot information set corresponding to the service big data record information, the following implementation manner may be implemented:

by a first one of the plurality of trace nodes having a trace association with each other, performing service characteristic tracking on the keyword clustering information serving the big data record information, fusing the tracking result with the clustering result output by the clustering node of the cross-node incidence relation of the first tracking node, taking the fused result as the tracking result of the first tracking node, and outputting the tracking result to the subsequent tracking nodes with tracking incidence relation, and continuously performing service feature tracking, fusion processing and tracking result output in the following tracking nodes with tracking association relation until the tracking nodes output the last tracking node, mapping the tracking result output by the last tracking node to a target hotspot information label, and taking the mapping result as a target hotspot information set corresponding to the service big data record information.

For another example, in another possible implementation manner, when the tracing program includes a plurality of tracing nodes having a tracing association relationship with each other, and when a cross-node association relationship exists between a tracing node and a cluster node at the same level, in the process of performing, by the tracing program, a tracing process based on a feature tag space on keyword cluster information of service big data record information to obtain a hot spot information feature tag set corresponding to the service big data record information, the following implementation manner may be implemented:

the method comprises the steps of carrying out service characteristic tracking on keyword clustering information of service big data record information through a first tracking node in a plurality of tracking nodes with tracking incidence relation, fusing a tracking result with a clustering result output by the clustering node with the cross-node incidence relation of the first tracking node, taking the fused result as the tracking result of the first tracking node and outputting the tracking result to a subsequent tracking node with the tracking incidence relation, continuing carrying out service characteristic tracking, fusion processing and tracking result output in the subsequent tracking node with the tracking incidence relation until the tracking result is output to the last tracking node, mapping the tracking result output by the last tracking node to a characteristic label space, and taking the mapping result as a hot spot information characteristic label set corresponding to the service big data record information.

Further, in a possible implementation manner, for step S130, in order to accurately fuse the hotspot title feature and the hotspot content feature to improve matching degree and experience degree of subsequent information distribution, the following exemplary sub-steps may be implemented, which are described in detail below.

In the substep S131, for each hotspot information feature tag in the hotspot information feature tag set, the tag feature value of the corresponding hotspot information feature tag in the classified service big data record information is fused with the tag feature value of the hotspot information feature tag in the hotspot information feature tag set, so as to obtain a first tag feature value of the hotspot information feature tag.

And a substep S132, performing weighting processing on the tag characteristic values of the hotspot information characteristic tags in the hotspot information characteristic tag set, and fusing the weighting processing result with the tag characteristic values of the corresponding hotspot information characteristic tags in the target hotspot information set to obtain second tag characteristic values of the hotspot information characteristic tags.

And a substep S133, performing weighting processing on the first tag characteristic value and the second tag characteristic value to obtain the characteristic of the hotspot information characteristic tag.

And a substep S134 of matching corresponding hot spot title characteristics from the classified service big data record information and matching corresponding hot spot content characteristics from the target hot spot information set according to the characteristics of the hot spot information characteristic labels.

And the substep S135, fusing the matched hot spot title characteristics and hot spot content characteristics to obtain a hot spot map node.

And a substep S136 of splicing all the hot spot map nodes according to the hot spot service relationship to obtain a hot spot information association map comprising target hot spot information.

Further, in one possible implementation manner, with respect to step S140, in order to improve the compatibility of subsequent information distribution and reduce noise content, the following exemplary sub-steps may be implemented, which are described in detail as follows.

And a substep S141, extracting a hot spot map node unit corresponding to each target hot spot information in the hot spot information association map, and extracting hot spot label feature vectors of the hot spot map node units in parallel while acquiring an original information hot spot service list associated by the hot spot map node units during pushing from a map data source of the hot spot map node units.

And the substep S142 of determining screening rule information for screening the original information hotspot service list based on the extracted hotspot tag feature vector, extracting rule matching parameters of a plurality of screening rule nodes to be used and service association information among different screening rule nodes from the screening rule information, and screening the plurality of screening rule nodes to be used according to the rule matching parameters and the service association information to obtain at least two target screening rule elements.

The coverage characteristic range of the rule matching parameters of the target screening rule elements is located in the set characteristic range, and the difference degree of the service association information between different target screening rule elements is smaller than a set value.

And a substep S143, screening the original information hotspot service list through the target screening rule element to obtain an information hotspot service list to be pushed.

And a substep S144, determining the hotspot tag compatible distribution of the information hotspot service list to be pushed according to the target hotspot tag feature vector determined from the preset subscription hotspot record, and determining the hotspot tag extended distribution of the information hotspot service list to be pushed according to the service tags in the determined information hotspot service list to be pushed.

And the substep S145, extracting key information hot spot information from the information hot spot service list to be pushed based on the hot spot tag compatible distribution and the hot spot tag extended distribution to obtain a key information hot spot information set, and respectively distributing the key information hot spot information set to a plurality of digital financial terminals 200.

In one possible implementation, for example, the substep S142 may be implemented by the following exemplary embodiments.

(1) Determining a plurality of feature vector sets with different theme types from the hotspot tag feature vectors, and constructing a first screening rule set and a second screening rule set according to the feature vector sets.

It should be noted that the first filtering rule set is a global filtering rule set, and the second filtering rule set is a specific object filtering rule set.

(2) And mapping the description vector corresponding to any one first screening rule in the first screening rule set to a second screening rule on a corresponding node in the second screening rule set, and determining the description vector mapping element information of the description vector in the second screening rule.

(3) And determining a target message queue commonly used by the hotspot tag feature vector in a set service range based on the hierarchical parameter between the description vector mapping element information and the target description information in the second screening rule, analyzing message queue content information corresponding to the target message queue, and generating screening rule information according to the information features indicated by the message queue content information.

(4) The screening rule information is listed in a topological structure to obtain a plurality of initial screening rule nodes, the screening hierarchy of each initial screening rule node is determined according to the topological relation hierarchy of the screening rule information, the initial screening rule nodes are sequenced according to the sequence of the screening hierarchies from large to small, and the initial screening rule nodes with the target number in the front of the sequence are selected as the screening rule nodes to be used.

(5) And aiming at each screening rule node to be used, determining component execution parameters and function calling parameters of a transaction distribution component of the screening rule node, determining a distribution rule use graph certificate of the screening rule node according to the component execution parameters, and extracting rule matching parameters from the distribution rule use graph certificate according to the function calling parameters.

(6) Calculating a rule coincidence parameter between every two screening rule nodes aiming at every two screening rule nodes in the plurality of screening rule nodes to be used, determining the image identification characteristic information of every two screening rule nodes on the service process based on the rule coincidence parameter, and extracting the service correlation information between every two screening rule nodes from the image identification characteristic information.

In one possible implementation, for example, the sub-step S143 can be implemented by the following exemplary embodiments.

(1) And determining the distribution of the screened message topics of the original information hotspot service list from the target screening rule elements.

The screening message topic distribution is used for representing topic distribution information of an original information hotspot service list in a hotspot graph node unit.

(2) And determining the topic matching parameters of the original information hotspot service list according to the topic distribution information in the screened message topic distribution, and acquiring the target topic matching parameters of the subscribed topic labels in the topic matching parameters.

(3) And screening the original information hot spot service list according to an inverse matrix of a distribution matrix corresponding to the screened message topic distribution, and screening a target data field corresponding to the content corresponding to the subscription topic tag of the target topic matching parameter in the original information hot spot service list by adopting the target topic matching parameter in the screening process to obtain the information hot spot service list to be pushed.

In one possible implementation, for example, the sub-step S144 may be implemented by the following exemplary embodiments.

(1) And extracting hotspot record information which does not change along with the update of the subscription hotspot record from a preset subscription hotspot record, extracting items to which the hotspot tags belong in the hotspot record information, and identifying the compatibility parameters generated when the items to which the hotspot tags belong are established from the items to which the hotspot tags belong.

(2) And determining a target hotspot tag feature vector from a preset subscription hotspot record according to the compatibility parameter, importing coding information corresponding to the target hotspot tag feature vector into a preset coding information list, and setting a compatible tag for the coding information imported into the coding information list each time.

(3) And determining the coding compatibility distribution coefficient between different pieces of coding information according to each piece of coding information in the coding information list and the coding weight of the coding information.

(4) And generating the hotspot tag compatible distribution of the information hotspot service list to be pushed according to each determined coding compatible distribution coefficient and the position of each coding compatible distribution coefficient in the coding information list.

(5) And determining an extended service tag corresponding to a service tag in the information hotspot service list to be pushed, and combining the service tag with the corresponding extended service tag to generate hotspot tag extended distribution of the information hotspot service list to be pushed.

In one possible implementation, the substep S145 may be implemented by the following exemplary embodiments.

(1) The method comprises the following steps of extracting key information hotspot information of an information hotspot service list to be pushed based on hotspot tag compatible distribution and hotspot tag extended distribution to obtain a key information hotspot information set, wherein the steps comprise:

(2) and carrying out service distribution on the information hotspot service list to be pushed based on hotspot tag expansion distribution to obtain a plurality of distribution service objects, and calculating the distribution service influence of each distribution service object according to the incidence relation between each distribution service object and other distribution service objects.

(3) And sequencing the shunting service objects according to the descending order of the influence of the shunting service to obtain a shunting service object sequencing set.

(4) And sequentially extracting key information hot spot information of each shunting service object in the sequencing set of the shunting service objects based on the compatibility distribution of the hot spot labels, and calculating the current hot spot influence parameters and the current compatibility distribution parameters of a group of key information hot spot information when each group of key information hot spot information is extracted.

(5) And when the current hotspot influence parameters and the current compatible distribution parameters meet set conditions, continuously extracting the key information hotspot information according to the sorting set of the shunting service objects.

(6) Judging whether the current hotspot influence parameter and the current compatible distribution parameter meet set conditions, deleting the current set of key information hotspot information and returning to traverse when the current hotspot influence parameter and the current compatible distribution parameter do not meet the set conditions, and extracting the key information hotspot information of the distribution service objects of the next sequencing sequence corresponding to the current set of key information hotspot information until the extraction of the key information hotspot information of all the distribution service objects in the distribution service object sequencing set is completed.

When it is determined in step (6) whether the current hotspot influence parameter and the current compatible distribution parameter meet the set condition, a first subscription frequency of the current hotspot influence parameter and a second subscription frequency of the current compatible distribution parameter may be determined according to the distribution coverage service of the split service object sorting set, and then the first subscription frequency and the second subscription frequency are compared.

For example, when the first subscription frequency is greater than the second subscription frequency, it is determined whether the current hotspot influence parameter exceeds a first preset value. And when the current hotspot influence parameter does not exceed the first preset value, judging whether the current compatible distribution parameter is lower than a second preset value, and when the current compatible distribution parameter is lower than the second preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter meet the set conditions. And when the current compatible distribution parameter is larger than or equal to a second preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet the set condition. And when the current hotspot influence parameter exceeds a first preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet the set condition. The first preset value and the second preset value are determined according to a first mapping value of a difference value of the first subscription frequency and the second subscription frequency in the first preset mapping list.

For another example, when the first subscription frequency is less than or equal to the second subscription frequency, it is determined whether the current hotspot influence parameter exceeds a third preset value. And when the current hotspot influence parameter does not exceed the third preset value, judging whether the current compatible distribution parameter is lower than a fourth preset value, and when the current compatible distribution parameter is lower than the fourth preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter meet set conditions. And when the current compatible distribution parameter is greater than or equal to the fourth preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet the set condition. And when the current hotspot influence parameter exceeds a third preset value, judging that the current hotspot influence parameter and the current compatible distribution parameter do not meet the set condition. The third preset value and the fourth preset value are determined according to second mapping values of the first subscription frequency and the second subscription frequency in a second preset mapping list respectively, and the first preset mapping list and the second preset mapping list are complementary lists.

Fig. 3 is a schematic diagram of functional modules of the keyword clustering apparatus 300 based on cloud computing and big data according to the embodiment of the present disclosure, and in this embodiment, the keyword clustering apparatus 300 based on cloud computing and big data may be divided into the functional modules according to the method embodiment executed by the blockchain financial cloud center 100, that is, the following functional modules corresponding to the keyword clustering apparatus 300 based on cloud computing and big data may be used to execute the method embodiments executed by the blockchain financial cloud center 100. The cloud computing and big data based keyword clustering apparatus 300 may include a classification processing module 310, a tracking processing module 320, a fusion processing module 330, and a distribution module 340, and the functions of the functional modules of the cloud computing and big data based keyword clustering apparatus 300 are described in detail below.

The classification processing module 310 is configured to perform classification processing on the service big data record information according to the information distribution rule and the distributed cloud computing task to obtain classified service big data record information, and perform keyword clustering on the classified service big data record information to obtain keyword clustering information of the service big data record information. The classification processing module 310 may be configured to perform the step S110, and as for a detailed implementation of the classification processing module 310, reference may be made to the detailed description of the step S110.

The tracking processing module 320 is configured to perform hotspot information tracking processing on the keyword cluster information of the service big data record information to obtain a target hotspot information set corresponding to the service big data record information, and perform feature tag tracking processing on the keyword cluster information of the service big data record information to obtain a hotspot information feature tag set corresponding to the service big data record information. The tracking processing module 320 may be configured to execute the step S120, and the detailed implementation of the tracking processing module 320 may refer to the detailed description of the step S120.

And the fusion processing module 330 is configured to perform fusion processing on the classified service big data record information and the target hotspot information set according to the hotspot information feature tag set to obtain a hotspot information association map including target hotspot information. The fusion processing module 330 may be configured to execute the step S130, and the detailed implementation of the fusion processing module 330 may refer to the detailed description of the step S130.

The distribution module 340 is configured to generate, according to a hotspot information association map including the target hotspot information, information hotspot information corresponding to the distribution of the hotspot information to the plurality of digital financial terminals 200. The distribution module 340 may be configured to perform the step S140, and the detailed implementation of the distribution module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the classification processing 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 classification processing 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 blockchain financial cloud center 100 for implementing the control device according to an embodiment of the present disclosure, and as shown in fig. 4, the blockchain financial cloud 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, the at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the classification processing module 310, the tracking processing module 320, the fusion processing module 330, and the distribution module 340 included in the cloud computing and big data based keyword clustering apparatus 300 shown in fig. 3), so that the processor 110 may execute the cloud computing and big data based keyword clustering method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiver 140 to perform transceiving actions, so as to perform data transceiving with the aforementioned digital financial terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the blockchain financial cloud center 100, which implement the similar principle and technical effect, and the detailed description of the embodiment is omitted here.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a global business interactive 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.

Also, the description uses specific words to describe embodiments of the description. 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. A keyword clustering method based on cloud computing and big data is applied to a block chain financial cloud center, the block chain financial cloud center is in communication connection with a plurality of digital financial terminals, a keyword clustering script for keyword clustering is configured in advance in the block chain financial cloud center and comprises a keyword clustering program and a tracking program, and the method comprises the following steps:

classifying the service big data record information according to an information distribution rule and an allocated cloud computing task to obtain classified service big data record information, and clustering the classified service big data record information based on keywords through the keyword clustering program to obtain keyword clustering information of the service big data record information;

tracking processing based on a feature tag space is carried out on the keyword clustering information of the service big data record information through the tracking program to obtain a hot spot information feature tag set corresponding to the service big data record information;

the information distribution rule comprises an information distribution and acquisition item subscribed by a service provider in advance, wherein the information distribution and acquisition item is a service type label referred to when subsequent information distribution is executed, and corresponding service big data record information is collected based on the service type label;

the cloud computing task comprises task nodes used for classifying service big data record information, specifically comprises trigger time nodes or trigger service nodes, classified service big data record information is obtained by classifying the service big data record information, and the specific classification processing mode is used for classifying and analyzing the service big data record information based on a pre-configured classification principle.

2. The keyword clustering method based on cloud computing and big data according to claim 1, wherein the tracking program comprises a plurality of tracking nodes having tracking association relationship with each other;

3. The keyword clustering method based on cloud computing and big data according to claim 1, wherein the tracking program comprises a plurality of tracking nodes having tracking association relationship with each other;

4. The method for clustering keywords based on cloud computing and big data according to claim 1, wherein the keyword clustering program comprises a plurality of clustering nodes having a tracking relationship with each other;

5. The method according to claim 1, wherein when the tracking program includes a plurality of tracking nodes having tracking association relationships with each other and when a cross-node association relationship exists between the tracking node and the clustering node at the same level, the tracking program performs hotspot-information-tag-based tracking processing on the keyword clustering information of the service big data record information to obtain a target hotspot information set corresponding to the service big data record information, the method includes:

6. The cloud computing and big data based keyword clustering method according to claim 1, wherein the method further comprises:

and splicing all the hot spot map nodes according to the hot spot service relationship to obtain a hot spot information association map comprising target hot spot information, so that corresponding information hot spot information is generated and distributed to the plurality of digital financial terminals according to the hot spot information association map comprising the target hot spot information.

7. The keyword clustering system based on cloud computing and big data is characterized by comprising a block chain financial cloud center and a plurality of digital financial terminals in communication connection with the block chain financial cloud center, wherein a keyword clustering script for keyword clustering is configured in advance in the block chain financial cloud center and comprises a keyword clustering program and a tracking program;

the blockchain financial cloud center is specifically configured to:

8. A computer-readable storage medium having stored therein instructions that, when executed, cause a computer to perform the cloud computing and big data based keyword clustering method of any one of claims 1 to 7.