CN115329204A - Cloud business service pushing method and pushing processing system based on big data mining - Google Patents
Cloud business service pushing method and pushing processing system based on big data mining Download PDFInfo
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Abstract
The embodiment of the invention provides a cloud service pushing method and a pushing processing system based on big data mining, which can be used for mining the portrait feature missing part of candidate cloud service pushing data in the portrait direction of a user group by comparing the portrait of the user group with the candidate cloud service pushing data of a target cloud service scene interest point, then adding the content of an auxiliary article based on the portrait feature missing part to obtain the information of the auxiliary pushing article related to each service pushing article, and further adding the information of the auxiliary pushing article of each service pushing article to improve the richness of the pushing content of the target cloud service scene interest point. And performing hotspot distribution analysis on the relation of each service push article of each candidate cloud service push data, thereby determining the relevant push support degree, controlling the information push of the target intelligent virtual service user group on the service push article based on the push support degree, and improving the reliability of the information push.
Description
The application is a divisional application of Chinese application with the application number of 202210218863.0, the application date of 2022, 03.08 and the title of the invention being "intelligent push processing method and system based on block chain and big data mining".
Technical Field
The invention relates to the technical field of cloud services, in particular to a cloud service pushing method and a pushing processing system based on big data mining.
Background
The cloud service technology and the artificial intelligence are fused, an intelligent ecological system is finally formed, and in the system, mutual fusion and intercommunication among different intelligent terminal devices, different system platforms and different application scenes are realized, so that all things are fused.
In the related technology, interest mining is generally performed by combining a cloud service linkage behavior database related to big data analysis, and in the deep learning optimization process of interest decision, the current optimization scheme does not consider allocation in a deep learning optimization stage, so that the feature learning effect is poor, the interest mining precision is influenced, and the subsequent precision of information push on a related target intelligent virtual service user group is influenced.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, the present invention provides a cloud service pushing method and a pushing processing system based on big data mining.
In a first aspect, the present invention provides an intelligent push processing method based on a block chain and big data mining, which is applied to an intelligent push processing system based on a block chain and big data mining, and the method includes:
acquiring a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database of a target smart virtual service user group;
mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on the selected cloud service scene interest mining model;
based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event so as to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event;
corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log group bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log group;
and in the distributed deep learning optimization stage, performing deep learning optimization of interest decision on the cloud service scene interest mining model sequentially based on each cloud service linkage event log group, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event logs based on the target interest mining model, and after obtaining corresponding target cloud service scene interest point distribution, performing information push on the target intelligent virtual service user group according to the target cloud service scene interest point distribution.
In a second aspect, an embodiment of the present invention further provides an intelligent push processing system based on blockchain and big data mining, where the intelligent push processing system based on blockchain and big data mining includes a processor and a machine-readable storage medium, where the machine-readable storage medium has stored therein machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to implement the foregoing intelligent push processing method based on blockchain and big data mining.
Based on any one of the above aspects, in the process of performing deep learning optimization of interest decision on the target interest mining model based on the cloud service linkage event, the cloud service linkage event logs in the cloud service linkage event are subjected to deep learning optimization stage distribution according to the feature learning cost, so that the feature learning effect can be improved when performing deep learning optimization of interest decision on the target interest mining model, the interest mining precision is improved, and the precision of information push on the related target intelligent virtual service user group subsequently is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings may be extracted based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an intelligent push processing method based on a block chain and big data mining according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a structure of an intelligent push processing system based on a blockchain and big data mining, which is provided in an embodiment of the present invention, and is used for implementing the above intelligent push processing method based on a blockchain and big data mining.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a particular application and its requirements. It will be apparent to those skilled in the art that various changes can be made in the embodiments disclosed, and that the general principles defined in this disclosure may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Thus, the present invention is not limited to the described embodiments, but should be accorded the widest scope consistent with the claims.
Fig. 1 is a schematic flow chart of an intelligent push processing method based on a block chain and big data mining according to an embodiment of the present invention, and details of the intelligent push processing method based on the block chain and big data mining are described below.
Step S110, a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements is obtained from a cloud service linkage behavior database of a target intelligent virtual service user group.
For example, keyword field data logs can be called for a plurality of cloud service linkage events sequentially or simultaneously, and the cloud service linkage event logs in the cloud service linkage events are determined based on the called keyword field data logs, so that deep learning optimization of interest decision is performed on the target interest mining model according to the cloud service linkage event logs of the cloud service linkage events.
Step S120, mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on a cloud service scene interest mining model.
For example, the cloud service scene interest mining model may perform event interest mining on each cloud service linkage event log in the cloud service linkage event by using a deep learning network model in any related technology, so as to obtain an event interest variable bound to each cloud service linkage event log.
Step S130, based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event to configure a cloud service linkage event log group corresponding to each cloud service linkage event binding.
For example, based on the event attention combination, the cloud service linkage event logs of the matched event attention features can be combined, so that the subsequent training effect is improved.
Step S140, corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log cluster bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log cluster.
And S150, performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group in sequence in the distributed deep learning optimization stage, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event log based on the target interest mining model, and performing information push on the target smart virtual service user group according to the target cloud service scene interest point distribution after obtaining the corresponding target cloud service scene interest point distribution.
Based on the above steps, in the process of performing deep learning optimization of interest decision on the target interest mining model based on the cloud service linkage event, the deep learning optimization stage distribution is performed on the cloud service linkage event logs in the cloud service linkage event according to the feature learning cost, so that the feature learning effect can be improved when performing deep learning optimization of interest decision on the target interest mining model, the interest mining precision is improved, and the precision of information push on the related target smart virtual service user group subsequently is improved.
In some embodiments, the aforementioned cloud business scenario interest mining model may include an event interest mining structure and an interest mining structure. Thus, in step S150, the following exemplary substeps may be implemented.
Step S151, transmitting the cloud service linkage event logs in each cloud service linkage event log group to the cloud service scene interest mining model, and outputting mining linkage interest points of each cloud service linkage event log.
Step S152, for all cloud service linkage event logs in each cloud service linkage event log group, carrying out loss analysis on mining linkage interest points of the cloud service linkage event logs and cloud service linkage interest points of the cloud service linkage events.
Step S153, if the mining linkage interest points of all the cloud service linkage event logs in any one cloud service linkage event log group have partial interest point loss, and/or if the mining linkage interest points of the cloud service linkage event logs in any one cloud service linkage event log group are different from the cloud service linkage interest points corresponding to the cloud service linkage events, updating model weight information of the interest mining structure and the cloud service scene interest mining model.
Step S154, based on the cloud service scene interest mining model and the interest mining structure updated by the model weight, obtaining iterative mining linkage interest points of all the cloud service linkage event logs in each cloud service linkage event log group, returning to the step of transmitting the cloud service linkage event logs in each cloud service linkage event log group to the cloud service scene interest mining model and outputting mining linkage interest points of all the cloud service linkage event logs until no interest point loss exists in the mining linkage interest points of all the cloud service linkage event logs in any cloud service linkage event log group, and outputting an interest mining model meeting the model deployment requirement.
In some embodiments, step S110 may be implemented by the following exemplary substeps.
Step S111, acquiring a cloud service linkage event which meets training deployment requirements and carries cloud service scene interest points from a cloud service linkage behavior database;
step S112, calling a key field data log of the cloud service linkage event according to a key field template of a linkage event log;
and S113, determining the data logs of the called key field as cloud service linkage event logs in the cloud service linkage event.
In some embodiments, in step S140, the determination scheme of the feature learning cost of each cloud service linkage event log group may be referred to in embodiments a and B described below.
Example A:
determining a global event attention variable from a cloud service linkage event log group, determining variable loss values of other event attention variables in the cloud service linkage event log group and the global event attention variable, obtaining an event attention variable with the maximum variable loss value between the cloud service linkage event log group and the global event attention variable, and determining the variable loss value between the maximum event attention variable and the global event attention variable as a learning cost value of the cloud service linkage event log group feature learning cost, wherein the variable loss value and the feature learning cost are in a positive correlation relationship.
Example B:
and determining a cyclic traversal quantity value corresponding to each cloud service linkage event log in each cloud service linkage event log group when the first interest mining of the cloud service linkage event log is confirmed based on the cloud service scene interest mining model, and obtaining a target reference coefficient of the cloud service linkage event log based on a preset reference coefficient corresponding to the cyclic traversal quantity value, wherein the cyclic traversal quantity value and the target reference coefficient are in a negative correlation relationship. And determining a target interest real value corresponding to each cloud service linkage event log group according to a target reference coefficient corresponding to each cloud service linkage event log of the cloud service linkage event log group. Determining the feature learning cost of each cloud service linkage event log group based on the interest real value corresponding to each cloud service linkage event log group, wherein the interest real value and the feature learning cost are in a negative correlation relationship.
In step S120, in some embodiments, the cloud service linkage event log in the cloud service linkage event may be input into an initial cloud service scene interest mining model, and deep learning optimization of an interest decision is performed on the initial cloud service scene interest mining model to obtain the cloud service scene interest mining model.
In step S130, in some embodiments, the present embodiment may sequentially or simultaneously combine event attention variables of cloud service linkage event logs included in each cloud service linkage event to obtain combined data of each cloud service linkage event.
In step S150, in some embodiments, some exemplary steps of pushing information of the target smart virtual service user group according to the target cloud service scenario interest point distribution may be implemented as follows.
Step S151, collecting at least one candidate cloud service pushing data matched with any one target cloud service scene interest point in the target cloud service scene interest point distribution;
step S152, comparing the user group portraits according to the candidate cloud service pushing data and the user group portraits of the target intelligent virtual service user group to obtain portraits characteristic missing parts related to each service pushing article in the candidate cloud service pushing data;
step S153, adding auxiliary article content to the missing part of the portrait characteristics related to each service push article in at least one candidate cloud service push data to obtain auxiliary push article information related to each service push article;
step S154, performing hotspot distribution analysis according to the candidate cloud service push data to acquire hotspot distribution information related to each service push article in the candidate cloud service push data;
step S155, determining pushing support degrees related to each service pushing article in the candidate cloud service pushing data according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to the auxiliary pushing article information;
step S156 determines the push configuration information related to the service push article according to the auxiliary push article information and the push support degree related to the same service push article in the at least one candidate cloud service push data, and performs information push of the target smart virtual service user group on the service push article based on the push configuration information.
Based on the above steps, in the embodiment, by performing user group portrait comparison on candidate cloud service pushed data of a target cloud service scene interest point, portrait feature missing parts of the candidate cloud service pushed data in the portrait direction of the user group can be mined, then auxiliary article content addition is performed based on the portrait feature missing parts, auxiliary pushed article information related to each service pushed article is obtained, and then the auxiliary pushed article information of each service pushed article is added, so that the pushed content richness of the target cloud service scene interest point can be improved. And performing hotspot distribution analysis on the relation of each service push article of each candidate cloud service push data, thereby determining the push support degree of each service push article in the candidate cloud service push data, controlling the information push of the target intelligent virtual service user group on the service push articles based on the push support degree, and improving the reliability of the information push.
In some embodiments, adding the content of an auxiliary article to the missing part of the portrait feature related to each service push article in at least one candidate cloud service push data to obtain the information of the auxiliary push article related to each service push article includes: acquiring tendency keyword distribution related to each service pushing article in the candidate cloud service pushing data according to the article keyword distribution related to each service pushing article in the candidate cloud service pushing data and the label pushing tendency characteristic corresponding to the candidate cloud service pushing data; acquiring auxiliary tendency keyword distribution related to each service pushing article according to auxiliary tendency characteristics related to the same service pushing article in at least one candidate cloud service pushing data; determining auxiliary push article information related to each service push article according to tendency keyword distribution and auxiliary tendency keyword distribution related to the same service push article in at least one candidate cloud service push data;
in some embodiments, determining, according to hotspot distribution information related to each service push article in the candidate cloud service push data and hotspot distribution information corresponding to the auxiliary push article information, a push support degree related to each service push article in the candidate cloud service push data includes: determining the weighted refreshing number related to each service pushing article in the candidate cloud service pushing data according to the article refreshing number related to each service pushing article in the candidate cloud service pushing data and the article refreshing number corresponding to the auxiliary pushing article information; determining the push support degree related to each service push article in the candidate cloud service push data according to the weighted refresh number and the preset push support degree corresponding relation related to each service push article in the candidate cloud service push data, wherein the preset push support degree corresponding relation comprises preset push support degrees corresponding to different refresh number ranges;
in some embodiments, the determining, according to the information and the push support degree of an auxiliary push article related to the same service push article in at least one candidate cloud service push data, push configuration information related to the service push article, and performing information push of the target smart virtual service user group on the service push article based on the push configuration information includes: determining derived extension strength for the auxiliary pushed article information according to the pushing support degree related to the same service pushed article in the at least one candidate cloud service pushing data; determining a target derived extension number of secondary push article information relevant for the service push article based on the derived extension strength; and performing derivative expansion on the auxiliary pushed article information based on the target derivative expansion quantity, taking the derivative expanded target auxiliary pushed article information as page auxiliary information of a pushed page corresponding to the service pushed article to obtain the pushed configuration information, and performing information pushing of the target intelligent virtual service user group on the service pushed article based on the pushed configuration information.
For example, in some embodiments, the target cloud traffic linkage event log may be obtained in the following manner.
Step W110, based on the acquired cloud service interaction requirement of the target cloud service user group, acquiring interaction requirement data of which the cloud service interaction requirement is matched with the subscription cloud service activity corresponding to the target cloud service user group.
In this embodiment, the target cloud service user group may be any cloud service user group, and the concept of the cloud service user group may be understood as a user group, for example, a family user group, a work user group, and the like. The cloud service interaction requirement can represent a cloud service requirement task initiated by the target cloud service user group, for example, a requirement task proposed for a certain family phase scene, such as a requirement task scene from home to sleep, or a requirement task proposed for a certain working scene, such as a requirement task proposed based on a certain work initiation plan. On this basis, interaction demand data matching the cloud service interaction demand with subscription cloud service activities corresponding to the target cloud service user group can be obtained. The subscription cloud service activity can be understood as a cloud service activity subscribed by a target cloud service user group based on self needs, such as a cloud service activity related to movie plates in an intelligent home. The interaction demand data can be called from a cloud user database based on the cloud service interaction demand and used for representing specific content data of the interaction demand.
And W120, generating an interactive demand variable based on the interactive demand data, transmitting the interactive demand variable to a cloud service linkage decision network meeting network convergence requirements, and obtaining cloud service linkage decision information and the support degree of the cloud service linkage decision information.
The cloud service linkage decision information may include target linkage decision information, and the target linkage decision information represents that the cloud service interaction demand can be used for linkage configuration. The linkage configuration means that a plurality of cloud service elements can be subjected to linkage interaction to realize a specific linkage interaction scene.
Step W130, if the cloud service linkage decision information is determined to be the target linkage decision information, analyzing whether linkage interaction demand information is carried in the interaction demand data or not to obtain analysis information, generating linkage support degree based on the support degree and the analysis information, and if the linkage support degree is determined not to be smaller than the target support degree, mining an interaction demand knowledge graph of the interaction demand data.
Step W140, performing linkage knowledge node extraction on the interaction demand knowledge graph to obtain a linkage knowledge node sequence, performing cloud service element extraction on the interaction demand knowledge graph to obtain a cloud service element sequence, mining a corresponding linkage interaction event from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine the linkage interaction event as a suggested linkage interaction event of the interaction demand data, generating a cloud service linkage behavior database based on the suggested linkage interaction event, and obtaining a target cloud service linkage event log.
Based on the above steps, in this embodiment, based on the obtained cloud service interaction demand of the target cloud service user group, interaction demand data matching the cloud service interaction demand and subscription cloud service activity corresponding to the target cloud service user group is obtained, an interactive demand variable is generated based on the interaction demand data, the interactive demand variable is transmitted to a cloud service linkage decision network meeting the network convergence requirement, and support of cloud service linkage decision information and cloud service linkage decision information is obtained, so that when the cloud service linkage decision information is determined to be the target linkage decision information, whether linkage interaction demand information is carried in the interaction demand data is analyzed, analysis information is obtained, linkage support is generated based on the support and the analysis information, if it is determined that the linkage support is not less than the target support, an interaction demand knowledge map of the interaction demand data is mined, linkage knowledge node extraction is performed on the interaction demand knowledge map, a linkage knowledge node sequence is obtained, and cloud service element extraction is performed on the interaction demand knowledge map, and a linkage service element sequence is obtained, and based on the linkage knowledge node sequence and the cloud service element sequence, the linkage knowledge map is preset linkage knowledge of the interaction demand interaction event sequence, which is determined as an interaction demand interaction recommendation of interaction event. Therefore, when the user further decides to carry linkage interaction demand information based on the interaction demand data of the target cloud service user group, the interaction demand knowledge graph of the interaction demand data is mined, the suggested linkage interaction events of the interaction demand data are determined according to the mining of the corresponding linkage interaction events from the preset linkage interaction event sequence and are uploaded to the corresponding block chain, and therefore on the premise that the actual interaction demand of the user is considered, the linkage interaction events are mined as far as possible so that the user can conveniently configure the cloud service, and the difficulty of manually configuring the cloud service linkage scene by the user can be reduced.
In an exemplary design idea, for step W120, in the process of generating an interactive demand variable based on the interactive demand data, in this embodiment, a target interactive demand item in the interactive demand data may be analyzed, each demand scenario rule in the target interactive demand item is read, a demand rule variable of the target interactive demand item is generated based on each demand scenario rule, data except the target interactive demand item in the interactive demand data is determined as candidate interactive data demand data, a cloud service demand node of the target interactive demand item in the interactive demand data is determined, and the interactive demand variable is obtained based on the cloud service demand node and the demand rule variable.
In an exemplary design idea, for step W130, this embodiment may obtain an interaction rule of the target interaction requirement item, determine an interaction scene feature corresponding to the interaction rule as a target interaction scene feature of the target interaction requirement item, and obtain a feature vector of the target interaction scene feature. And then, matching the feature vector with all linkage feature vectors in a preset linkage feature vector, and if the feature vector is determined to be matched with any one linkage feature vector, outputting the analysis information as the linkage interaction demand information carried in the interaction demand data.
In an exemplary design concept, for step W130, in the process of parsing the interaction requirement knowledge graph of the interaction requirement data, the following exemplary sub-steps may be implemented.
Substep W131, analyzing the interaction demand fragment data of each interaction demand fragment in each interaction demand in the interaction demand data to obtain an interaction demand fragment data set of each interaction demand;
substep W132, performing cloud service simulation operation on past cloud service configuration data of the interaction demand segment data set and a to-be-generated knowledge graph according to a plurality of cloud service software scenes to obtain cloud service simulation operation data of the interaction demand segment data set in the to-be-generated knowledge graph, where the cloud service software scenes of the interaction demand segment data set include at least one;
substep W133, corresponding to each interactive demand fragment data set, respectively performing cloud service knowledge entity identification on the corresponding past cloud service configuration data and cloud service simulation operation data to obtain a cloud service knowledge entity of the past cloud service configuration data and a cloud service knowledge entity of the cloud service simulation operation data;
substep W134, obtaining entity association attributes between cloud service knowledge entities of past cloud service configuration data and cloud service knowledge entities of cloud service simulation operation data corresponding to the same interaction requirement fragment data set, extracting cloud service simulation operation data of which the entity association attributes meet preset entity association attributes from the cloud service simulation operation data of the interaction requirement fragment data set, and obtaining first cloud service simulation operation data of each interaction requirement fragment data set;
substep W135, determining connectivity of cloud service activity information of corresponding past cloud service configuration data and first cloud service simulation operation data, corresponding to each interactive demand segment data set;
substep W136, extracting cloud service simulation operation data with the connectivity of the cloud service activity information being greater than the target connectivity from the first cloud service simulation operation data of each interactive demand segment data set, to obtain second cloud service simulation operation data of each interactive demand segment data set;
and a substep W137, determining target key knowledge entities of the interaction demand fragment data sets and knowledge entity attributes among the key knowledge entities according to the second cloud service simulation operation data of each interaction demand fragment data set, so as to construct a corresponding interaction demand knowledge graph.
In an exemplary design idea, in step W140, this embodiment may analyze the knowledge entities that are intersected in the interaction demand knowledge graph to obtain a linkage knowledge node sequence, extract a cloud service equipment object covered by each knowledge entity in the interaction demand knowledge graph to obtain a cloud service element sequence, and mine a corresponding linkage interaction event from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine a suggested linkage interaction event of the interaction demand data, and upload the suggested linkage interaction event to a corresponding block chain.
Each preset linkage interaction event in the preset linkage interaction event sequence is provided with a corresponding linkage knowledge node and a corresponding cloud service element, the linkage knowledge node sequence and the cloud service element sequence are matched with the linkage knowledge node and the cloud service element covered by each preset linkage interaction event in the preset linkage interaction event sequence, and the suggested linkage interaction event of the interaction demand data is obtained based on the matching result.
In an exemplary design idea, the embodiment of the present application further provides a training method for a cloud service linkage decision network, which includes the following steps.
Step W101, obtaining an initialized neural network model, wherein the initialized neural network model comprises a decision model unit;
step W102, acquiring reference collection data, wherein the reference collection data comprises reference collection interaction demand data, reference linkage decision information and corresponding demand support;
step W103, dividing the reference collected data into a first data set and a second data set, optimizing weight information in the decision model unit based on the first data set to obtain a training linkage decision network, and determining decision cost information of the training linkage decision network based on the second data set;
step W104, if the decision cost information is determined to be larger than the target decision cost, continuously optimizing the weight information of the training linkage decision network based on the second data set until the decision cost information of the training linkage decision network is not smaller than or equal to the target decision cost, and obtaining the cloud service linkage decision network.
In an exemplary design idea, in a process of generating a linkage support degree based on the support degree and the resolution information, a first influence coefficient of the cloud service linkage decision network may be obtained, and the first support degree of the interaction demand data is determined based on the support degree and a weighted support degree of the first influence coefficient. Then, a metric value corresponding to the analysis information is obtained, a second influence coefficient of the linkage interaction demand information is obtained, a second support degree of the interaction demand data is determined based on the metric value and the weighting support degree of the second influence coefficient, an adding support degree of the first support degree and the second support degree is calculated, and the linkage support degree is obtained.
Fig. 2 illustrates a hardware structure of the intelligent push processing system 100 based on blockchain and big data mining for implementing the intelligent push processing method based on blockchain and big data mining based on blockchain technology according to an embodiment of the present application, and as shown in fig. 2, the intelligent push processing system 100 based on blockchain and big data mining may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.
In one possible design, the intelligent push processing system 100 based on blockchain and big data mining may be a single server or a group of servers. The server group may be centralized or distributed (e.g., intelligent push processing system 100 based on blockchains and big data mining may be a distributed system). In some embodiments, the intelligent push processing system 100 based on blockchain and big data mining may be local or remote. For example, the intelligent push processing system 100 based on blockchain and big data mining may access information and/or data stored in the machine-readable storage medium 120 via a network. As another example, the intelligent push processing system 100 based on blockchain and big data mining may be directly connected to the machine-readable storage medium 120 to access stored information and/or data. In some embodiments, the intelligent push processing system 100 based on blockchain and big data mining may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
Machine-readable storage medium 120 may store data and/or instructions. In some embodiments, the machine-readable storage medium 120 may store data obtained from an external terminal. In some embodiments, the machine-readable storage medium 120 may store data and/or instructions for use by or in connection with the blockchain and big data mining based intelligent push processing system 100 to perform or perform the exemplary methods described in this application. In some embodiments, the machine-readable storage medium 120 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read-write memories can include Random Access Memory (RAM). Exemplary RAM may include active random access memory (DRAM), double data rate synchronous active random access memory (DDR SDRAM), passive random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM), among others. Exemplary read-only memories may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (perrom), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the machine-readable storage medium 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the intelligent push processing method based on blockchain and big data mining based on blockchain technology according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the communication unit 140.
For a specific implementation process of the processor 110, reference may be made to the above embodiments of the method executed by the intelligent push processing system 100 based on block chaining and big data mining, which have similar implementation principles and technical effects, and details of this embodiment are not described herein again.
In addition, an embodiment of the present application further provides a readable storage medium, where the readable storage medium has preset therein computer-executable instructions, and when a processor executes the computer-executable instructions, the method for processing intelligent push based on blockchain and big data mining based on blockchain technology as described above is implemented.
It should be understood that the above description is intended for illustrative purposes only, and is not intended to limit the scope of the present disclosure. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the description of the invention. However, such modifications and variations do not depart from the scope of the present invention.
While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art in view of this disclosure that the above disclosure is intended to be exemplary only and is not intended to limit the invention. Various modifications, improvements and adaptations to the present invention may occur to those skilled in the art, though not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.
Also, the present invention has been described using specific terms to describe embodiments of the invention. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the invention. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the invention may be combined as suitable.
Moreover, those skilled in the art will recognize that aspects of the present invention may be illustrated and described in terms of several patentable species or situations, including any new and useful process, machine, article, or material combination, or any new and useful modification thereof. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module", or "system". Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media, wherein the computer-readable program code is embodied therein.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination thereof.
Computer program code required for operation of various portions of the present invention may be written in any one or more of a variety of programming languages, including a subject 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, an active 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, partly on the user's computer, partly on a remote computer, or entirely on the remote computer or on an intelligent push processing system based on blockchains and big data mining. 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 of processing elements and sequences, the use of numerical letters, or other designations herein, is not intended to limit the order of the processes and methods 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 should 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 of the invention. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing intelligent push processing system or mobile device based on blockchain and big data mining.
Similarly, it should be noted that in the preceding description of embodiments of the invention, 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. Similarly, it should be noted that in the preceding description of embodiments of the invention, 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.
Claims (10)
1. A cloud service pushing method based on big data mining is characterized by being applied to an intelligent pushing processing system based on a block chain and big data mining, and comprising the following steps:
collecting at least one candidate cloud service push data matched with any target cloud service scene interest point in target cloud service scene interest point distribution;
comparing the portrait of the user group according to the push data of each candidate cloud service and the portrait of the user group of the target intelligent virtual service user group to obtain a portrait characteristic missing part related to each service push article in the push data of the candidate cloud service;
adding auxiliary article content to the missing part of portrait characteristics related to each service push article in at least one candidate cloud service push data to obtain auxiliary push article information related to each service push article;
performing hotspot distribution analysis according to the candidate cloud service pushing data to acquire hotspot distribution information related to each service pushing article in the candidate cloud service pushing data;
determining pushing support degrees related to each service pushing article in the candidate cloud service pushing data according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to the auxiliary pushing article information;
determining pushing configuration information related to the service pushing article according to auxiliary pushing article information and pushing support degree related to the same service pushing article in at least one candidate cloud service pushing data, and pushing information of the target intelligent virtual service user group to the service pushing article based on the pushing configuration information.
2. The cloud service pushing method based on big data mining according to claim 1, wherein the adding of auxiliary article contents is performed on missing parts of portrait features related to each service pushing article in at least one candidate cloud service pushing data to obtain auxiliary pushing article information related to each service pushing article, and includes:
acquiring tendency keyword distribution related to each service pushing article in the candidate cloud service pushing data according to the article keyword distribution related to each service pushing article in the candidate cloud service pushing data and the label pushing tendency characteristic corresponding to the candidate cloud service pushing data;
acquiring auxiliary tendency keyword distribution related to each service pushing article according to auxiliary tendency characteristics related to the same service pushing article in at least one candidate cloud service pushing data;
and determining auxiliary pushing article information related to each service pushing article according to tendency keyword distribution and auxiliary tendency keyword distribution related to the same service pushing article in at least one candidate cloud service pushing data.
3. The cloud service pushing method based on big data mining according to claim 1, wherein the determining, according to hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and hotspot distribution information corresponding to auxiliary pushing article information, a pushing support degree related to each service pushing article in the candidate cloud service pushing data comprises:
determining the weighted refreshing number related to each service pushing article in the candidate cloud service pushing data according to the article refreshing number related to each service pushing article in the candidate cloud service pushing data and the article refreshing number corresponding to the auxiliary pushing article information;
determining the push support degree related to each service push article in the candidate cloud service push data according to the weighted refresh number related to each service push article in the candidate cloud service push data and the corresponding relation of the preset push support degree, wherein the corresponding relation of the preset push support degree comprises the preset push support degrees corresponding to different refresh number ranges.
4. The cloud service pushing method based on big data mining according to claim 1, wherein the determining of pushing configuration information related to the service pushing article according to pushing support and auxiliary pushing article information related to a same service pushing article in at least one candidate cloud service pushing data and the pushing configuration information, and performing information pushing of the target smart virtual service user group on the service pushing article based on the pushing configuration information includes:
determining derived extension strength aiming at the auxiliary article pushing information according to pushing support degrees related to the same article pushing service in the candidate cloud service pushing data;
determining a target derived extension number of secondary push article information relevant for the service push article based on the derived extension strength;
and performing derivative expansion on the auxiliary pushed article information based on the target derivative expansion quantity, taking the derivative expanded target auxiliary pushed article information as page auxiliary information of a pushed page corresponding to the service pushed article to obtain the pushed configuration information, and performing information pushing of the target intelligent virtual service user group on the service pushed article based on the pushed configuration information.
5. The big data mining-based cloud business service pushing method according to claim 1, wherein the method further comprises:
acquiring a cloud service linkage event log in a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database of a target intelligent virtual service user group;
mining event attention variables of cloud service linkage event logs in each cloud service linkage event based on the selected cloud service scene interest mining model;
based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of cloud service linkage event logs of each cloud service linkage event so as to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event;
corresponding to each cloud service linkage event, according to the characteristic learning cost descending information of the cloud service linkage event logs in the corresponding cloud service linkage event log group bound by the cloud service linkage event logs, allocating a deep learning optimization stage for the corresponding cloud service linkage event log group;
and in the distributed deep learning optimization stage, performing deep learning optimization of interest decision on the cloud service scene interest mining model based on each cloud service linkage event log group in sequence, outputting a target interest mining model which finally meets the model deployment requirement, performing interest mining on the input target cloud service linkage event logs based on the target interest mining model, and obtaining corresponding target cloud service scene interest point distribution.
6. The cloud service pushing method based on big data mining as claimed in claim 5, wherein the target cloud service linkage event log is obtained by:
acquiring interaction demand data matched with the cloud service subscription activity corresponding to the target cloud service user group according to the acquired cloud service interaction demand of the target cloud service user group;
generating an interactive demand variable based on the interactive demand data, transmitting the interactive demand variable to a cloud service linkage decision network meeting network convergence requirements, and obtaining cloud service linkage decision information and support of the cloud service linkage decision information, wherein the cloud service linkage decision information comprises target linkage decision information, and the target linkage decision information represents that the cloud service interactive demand can be used for linkage configuration;
if the cloud service linkage decision information is determined to be the target linkage decision information, analyzing whether linkage interaction demand information is carried in the interaction demand data or not to obtain analysis information, generating linkage support degree based on the support degree and the analysis information, and if the linkage support degree is determined not to be smaller than the target support degree, mining an interaction demand knowledge graph of the interaction demand data;
linkage knowledge node extraction is carried out on the interaction demand knowledge graph to obtain a linkage knowledge node sequence, cloud service element extraction is carried out on the interaction demand knowledge graph to obtain a cloud service element sequence, corresponding linkage interaction events are mined from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine the linkage interaction events as suggested linkage interaction events of the interaction demand data, a cloud service linkage behavior database is generated based on the suggested linkage interaction events, and a target cloud service linkage event log is obtained.
7. The cloud business service pushing method based on big data mining as claimed in claim 6, wherein the generating of the interoperable demand variable based on the interactive demand data comprises:
analyzing a target interaction demand item in the interaction demand data, and reading each demand scene rule in the target interaction demand item;
generating a demand rule variable of the target interaction demand item based on each demand scene rule;
determining data except the target interaction demand item in the interaction demand data as candidate interaction data demand data;
determining a cloud service demand node of the target interaction demand item in the interaction demand data;
and acquiring the interactive demand variable based on the cloud service demand node and the demand rule variable.
8. The cloud service pushing method based on big data mining according to claim 7, wherein the analyzing whether the interaction demand data carries linkage interaction demand information includes:
acquiring an interaction rule of the target interaction demand item, determining an interaction scene feature corresponding to the interaction rule as a target interaction scene feature of the target interaction demand item, and acquiring a feature vector of the target interaction scene feature;
and matching the feature vector with all linkage feature vectors in a preset linkage feature vector, and if the feature vector is determined to be matched with any one linkage feature vector, outputting the analysis information as the linkage interaction demand information carried in the interaction demand data.
9. The cloud service pushing method based on big data mining according to claim 7, wherein the step of parsing the interaction requirement knowledge graph of the interaction requirement data includes:
analyzing the interaction demand fragment data of each interaction demand fragment in each interaction demand in the interaction demand data to obtain an interaction demand fragment data set of each interaction demand;
performing cloud service simulation operation on past cloud service configuration data of the interaction demand fragment data set and a to-be-generated knowledge graph according to a plurality of cloud service software scenes to obtain cloud service simulation operation data of the interaction demand fragment data set in the to-be-generated knowledge graph, wherein the cloud service software scenes of the interaction demand fragment data set comprise at least one;
corresponding to each interactive demand fragment data set, cloud service knowledge entity recognition is respectively carried out on corresponding past cloud service configuration data and cloud service simulation operation data, and a cloud service knowledge entity of the past cloud service configuration data and a cloud service knowledge entity of the cloud service simulation operation data are obtained;
acquiring entity association attributes between cloud service knowledge entities of past cloud service configuration data and cloud service knowledge entities of cloud service simulation operation data corresponding to the same interaction demand fragment data set, extracting cloud service simulation operation data of which the entity association attributes meet preset entity association attributes from the cloud service simulation operation data of the interaction demand fragment data set, and acquiring first cloud service simulation operation data of each interaction demand fragment data set;
determining connectivity of cloud service activity information of corresponding past cloud service configuration data and first cloud service simulation operation data corresponding to each interaction demand fragment data set;
extracting cloud service simulation operation data of which the connectivity of the cloud service activity information is greater than a target connectivity from first cloud service simulation operation data of each interactive demand fragment data set to obtain second cloud service simulation operation data of each interactive demand fragment data set;
and determining target key knowledge entities of the interactive demand fragment data sets and knowledge entity attributes among the key knowledge entities according to the second cloud service simulation operation data of each interactive demand fragment data set so as to construct a corresponding interactive demand knowledge graph.
10. An intelligent pushing processing system based on block chain and big data mining, which is characterized in that the intelligent pushing processing system based on block chain and big data mining comprises a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to realize the big data mining-based cloud business service pushing method according to any one of claims 1 to 9.
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| CN115905702A (en) * | 2022-12-06 | 2023-04-04 | 鄄城县馨宁网络科技有限公司 | Data recommendation method and system based on user demand analysis |
| CN116070111A (en) * | 2022-11-17 | 2023-05-05 | 北京东方通科技股份有限公司 | Auxiliary decision method and system for big data mining based on AI |
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| CN114840486B (en) * | 2022-06-28 | 2022-09-16 | 广州趣米网络科技有限公司 | User behavior data acquisition method and system and cloud platform |
| CN114969552B (en) * | 2022-07-06 | 2023-04-18 | 中图数字科技(北京)有限公司 | Big data mining method and AI prediction system for personalized information push service |
| CN115455300B (en) * | 2022-09-29 | 2023-04-14 | 临沂沂川网络科技有限公司 | Data pushing method and system based on artificial intelligence and cloud platform |
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| CN108648031B (en) * | 2018-03-28 | 2022-01-18 | 苏州千照信息科技有限公司 | Product recommendation method and device |
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| CN111028006B (en) * | 2019-12-02 | 2023-07-14 | 支付宝(杭州)信息技术有限公司 | Service delivery auxiliary method, service delivery method and related device |
| CN113392173A (en) * | 2020-08-28 | 2021-09-14 | 郭举 | Information push updating method and system based on block chain and cloud service information platform |
| CN112600893A (en) * | 2020-12-04 | 2021-04-02 | 褚萌萌 | Software application data mining method based on big data positioning and software service platform |
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| CN113361794A (en) * | 2021-06-21 | 2021-09-07 | 深圳市鸿业电线有限公司 | Information pushing method and AI (Artificial Intelligence) pushing system based on Internet e-commerce big data |
| CN113332729A (en) * | 2021-07-08 | 2021-09-03 | 创联无忧(广州)信息科技有限公司 | Cloud game vulnerability detection method based on deep learning and artificial intelligence server |
| CN113409096B (en) * | 2021-08-19 | 2021-11-16 | 腾讯科技（深圳）有限公司 | Target object identification method and device, computer equipment and storage medium |
| CN113761033B (en) * | 2021-09-13 | 2022-03-25 | 江苏楚风信息科技有限公司 | Information arrangement method and system based on file digital management |
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| CN114117235A (en) * | 2021-12-07 | 2022-03-01 | 绥化市纯互联网商务有限公司 | E-commerce content pushing method adopting artificial intelligence analysis and E-commerce big data system |
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| CN116070111A (en) * | 2022-11-17 | 2023-05-05 | 北京东方通科技股份有限公司 | Auxiliary decision method and system for big data mining based on AI |
| CN116070111B (en) * | 2022-11-17 | 2023-08-04 | 北京东方通科技股份有限公司 | Auxiliary decision method and system for big data mining based on AI |
| CN115905702A (en) * | 2022-12-06 | 2023-04-04 | 鄄城县馨宁网络科技有限公司 | Data recommendation method and system based on user demand analysis |
| CN115905702B (en) * | 2022-12-06 | 2023-10-10 | 雨果跨境(厦门)科技有限公司 | Data recommendation method and system based on user demand analysis |
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