CN114564566A - Application cloud service linkage big data processing method and cloud service artificial intelligence system - Google Patents
Application cloud service linkage big data processing method and cloud service artificial intelligence system Download PDFInfo
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Abstract
The embodiment of the invention provides an application cloud service linkage big data processing method and a cloud service artificial intelligence system, wherein when interaction demand data based on a target cloud service user group is further decided to carry linkage interaction demand information, an interaction demand knowledge graph of the interaction demand data is mined, and a suggested linkage interaction event which is determined as the interaction demand data is determined according to a linkage interaction event corresponding to the mining from a preset linkage interaction event sequence and is uploaded to a corresponding block chain, so that the linkage interaction event is mined as far as possible to facilitate the user to carry out cloud service configuration on the premise of considering the actual interaction demand of the user, and the difficulty of manually configuring a cloud service linkage scene by the user can be reduced.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and cloud services, in particular to a cloud service linkage big data processing method and a cloud service artificial intelligence system.
Background
In the related art, a user is often required to perform cloud service configuration aiming at a linkage interaction event in a cloud service interaction process, the difficulty of manually configuring a cloud service linkage scene by the user is high, and the actual cloud service interaction requirements of the user cannot be comprehensively combined.
Disclosure of Invention
In order to overcome at least the defects in the prior art, the invention aims to provide an application cloud service linkage big data processing method and a cloud service artificial intelligence system.
In a first aspect, the invention provides a method for processing application cloud service linkage big data, which is applied to a cloud service artificial intelligence system, and the method comprises the following steps:
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;
and 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, and uploading the suggested linkage interaction event to a corresponding block chain.
In a second aspect, an embodiment of the present invention further provides a cloud service artificial intelligence system, where the cloud service artificial intelligence system includes a processor and a machine-readable storage medium, where the machine-readable storage medium stores machine-executable instructions, and the machine-executable instructions are loaded and executed by the processor to implement the foregoing application cloud service linkage big data processing method.
Based on any one of the above aspects, when the interaction demand data based on the target cloud service user group is further decided to carry linkage interaction demand information, an interaction demand knowledge graph of the interaction demand data is mined, suggested linkage interaction events of the interaction demand data are determined in sequence according to corresponding linkage interaction events mined from a preset linkage interaction event sequence and uploaded to corresponding block chains, 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 cloud services, and the difficulty of manually configuring a cloud service linkage scene by the user can be reduced.
Drawings
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 flow chart of a method for processing application cloud service linkage big data according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a cloud service artificial intelligence system for implementing the method for processing application cloud service linkage big data according to the embodiment of the present invention.
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.
The terminology used in the description of the invention herein is for the purpose of describing particular example embodiments only and is not intended to limit the scope of the present invention. As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, components, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, and/or groups thereof.
These and other features and characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the accompanying drawings, all of which form a part of this specification. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.
Flow charts are used in the present invention to illustrate the operations performed by systems according to some embodiments of the present invention. It should be understood that the operations in the flow diagrams may be performed out of order. Rather, various steps may be processed in reverse order or simultaneously. Further, one or more other operations may be added to the flowchart. One or more operations may also be deleted from the flowchart.
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments.
Fig. 1 is a schematic flow chart of a method for processing application cloud service-linked big data according to an embodiment of the present invention, and the method for processing application cloud service-linked big data is described in detail below.
Step S110, acquiring interaction demand data matched with the cloud service interaction demand and the subscription cloud service activity corresponding to the target cloud service user group based on the acquired cloud service interaction demand of 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, such as 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 between returning home and sleeping, or a requirement task proposed for a certain work 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 requirements, such as a cloud service activity related to a movie block 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 step S120, generating an interactive demand variable based on the interactive demand data, transmitting the interactive demand variable to a cloud service linkage decision network meeting the network convergence requirement, 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 S130, 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 S140, 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, and uploading the suggested linkage interaction event to a corresponding block chain.
Based on the above steps, the embodiment obtains interaction demand data matching the cloud service interaction demand with the subscription cloud service activity corresponding to the target cloud service user group based on the obtained cloud service interaction demand of the target cloud service user group, generates an interactive demand variable based on the interaction demand data, transmits the interactive demand variable to a cloud service linkage decision network satisfying the network convergence requirement, obtains cloud service linkage decision information and the support degree of the cloud service linkage decision information, thereby determining that the cloud service linkage decision information is the target linkage decision information, resolving whether the linkage interaction demand data carries the linkage interaction demand information, obtaining resolution information, generating linkage support degree based on the support degree and the resolution information, if determining that the linkage support degree is not less than the target support degree, mining an interaction demand knowledge graph of the interaction demand data, and performing linkage knowledge node extraction on the interaction demand knowledge graph, the method comprises the steps of obtaining a linkage knowledge node sequence, carrying out cloud service element extraction on an interaction demand knowledge graph spectrum 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, and determining the linkage interaction event as a suggested linkage interaction event of interaction demand data. 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 S120, 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 S130, this embodiment may obtain an interaction rule of the target interaction demand item, determine an interaction scene feature corresponding to the interaction rule as a target interaction scene feature of the target interaction demand 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 idea, for step S130, in the process of parsing the interaction requirement knowledge graph of the interaction requirement data, the following exemplary sub-steps may be implemented.
The substep S131 is used for 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 S132, 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, wherein the cloud service software scenes of the interaction demand segment data set include at least one;
substep S133, corresponding to each interaction demand fragment data set, respectively performing cloud service knowledge entity recognition on 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 S134, 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;
substep S135, 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 segment data set;
substep S136, 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 S137, 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 S140, 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 device 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, and the suggested linkage interaction event of the interaction demand data is obtained based on a matching result by matching the linkage knowledge node sequence and the cloud service element sequence with the linkage knowledge node and the cloud service element covered by each preset linkage interaction event in the preset linkage interaction event sequence.
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 S101, obtaining an initialized neural network model, wherein the initialized neural network model comprises a decision model unit;
step S102, acquiring reference collection data, wherein the reference collection data comprises reference collection interaction demand data, reference linkage decision information and corresponding demand support degree;
step S103, 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;
and step S104, 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 analysis 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 weighting 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, and the added support degree of the first support degree and the second support degree is calculated to obtain the linkage support degree.
In an exemplary design concept, based on the above description, the present embodiment may further include the following steps.
Step S210, based on a cloud service linkage behavior database generated by the target cloud service user group based on the suggested linkage interaction event of the interaction demand data, performing interest mining on an input target cloud service linkage event log based on a target interest mining model, and obtaining corresponding target cloud service scene interest point distribution.
Step S220, 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 is collected.
Step S230, comparing the user group portraits according to the candidate cloud service pushing data and the user group portraits of the target cloud service user group, and obtaining portrait feature missing parts related to each service pushing article in the candidate cloud service pushing data.
Step S240, adding the content of an auxiliary article to the missing part of the portrait feature related to each service push article in the at least one candidate cloud service push data, to obtain the information of the auxiliary push article related to each service push article.
Step S250, performing hotspot distribution analysis according to the candidate cloud service pushing data, and acquiring hotspot distribution information related to each service pushing article in the candidate cloud service pushing data.
Step S260, determining a pushing support degree related to each service pushing article in the candidate cloud service pushing data according to the hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and the hotspot distribution information corresponding to the auxiliary pushing article information.
Step S270, determining 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 at least one candidate cloud service push data, and performing information push of the target cloud 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, performing information push of the target cloud service user group on the service push articles based on push support degree interaction, and improving the reliability of information push.
In some embodiments, the adding of 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 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 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;
in some embodiments, determining a pushing support degree 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 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 pushing support degree related to each service pushing article in the candidate cloud service pushing data according to the corresponding relation between the weighted refreshing number of each service pushing article in the candidate cloud service pushing data and the preset pushing support degree, wherein the corresponding relation of the preset pushing support degree comprises the preset pushing support degrees corresponding to different refreshing number ranges;
in some embodiments, the determining, 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, push configuration information related to the service push article, and performing information push of the target cloud 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 push configuration information, and performing information push of the target cloud service user group on the service pushed article based on the push configuration information.
For example, step S210 can be implemented by the following steps.
Step W110, obtaining a cloud service linkage event log in the cloud service linkage event carrying the cloud service scene interest points meeting the training deployment requirement from a cloud service linkage behavior database of the 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.
And step W120, mining event attention variables of cloud service linkage event logs in the cloud service linkage events 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.
And step W130, based on each cloud service linkage event as a training member, performing event attention combination on event attention variables of the cloud service linkage event logs of each cloud service linkage event to configure cloud service linkage event log groups corresponding to binding of each cloud service linkage event.
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.
And step W140, 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 W150, 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 logs based on the target interest mining model, and 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 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 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, step W150 can be implemented by the following exemplary substeps.
And step W151, 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 W152, for all cloud service linkage event logs in each cloud service linkage event log group, performing loss analysis on the mining linkage interest points of the cloud service linkage event logs and the cloud service linkage interest points of the cloud service linkage event.
Step W153, 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 W154, 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 in any cloud service linkage event log group 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 W110 may be implemented by the following exemplary substeps.
Step W111, acquiring a cloud service linkage event carrying cloud service scene interest points meeting training deployment requirements from a cloud service linkage behavior database;
step W112, calling a key field data log of the cloud service linkage event according to a key field template of a linkage event log;
step W113, determining the cloud service linkage event log in the cloud service linkage event based on the called keyword segment data log.
In some embodiments, in step W140, 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 W120, 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 W130, 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.
Fig. 2 illustrates a hardware structural diagram of a cloud business artificial intelligence system 100 for implementing the above method for processing big data in linkage with cloud business based on a blockchain technology, according to an embodiment of the present disclosure, as shown in fig. 2, the cloud business artificial intelligence system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.
In one possible design, the cloud service artificial intelligence system 100 may be a single server or a group of servers. The set of servers may be centralized or distributed (e.g., the cloud services artificial intelligence system 100 may be a distributed system). In some embodiments, the cloud services artificial intelligence system 100 can be local or remote. For example, the cloud services artificial intelligence system 100 can access information and/or data stored in the machine-readable storage medium 120 via a network. As another example, cloud services artificial intelligence system 100 can be directly connected to machine-readable storage medium 120 to access stored information and/or data. In some embodiments, the cloud services artificial intelligence system 100 can 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, machine-readable storage medium 120 may store data and/or instructions used by cloud services artificial intelligence system 100 to perform or to use to accomplish 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 memories may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and so forth. Exemplary volatile read and 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 method for processing application cloud service linkage big data based on the 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-mentioned method embodiments executed by the cloud service artificial intelligence system 100, and implementation principles and technical effects are similar, which are not described herein again.
In addition, an embodiment of the present application further provides a readable storage medium, where a computer-executable instruction is preset in the readable storage medium, and when a processor executes the computer-executable instruction, the method for processing application cloud service linkage big data based on the block chain technology is implemented.
It should be understood that the foregoing description is for purposes of illustration 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, some of the features, structures, or characteristics of one or more embodiments of the present 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, with computer-readable program code 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 on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on the remote computer or a cloud services artificial intelligence system. 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 sequences of the process are described, the use of letters or other designations herein is not intended to limit the order of the processes and methods of the invention unless otherwise indicated by 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 cloud services artificial intelligence system or mobile device.
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 method for processing application cloud service linkage big data is applied to a cloud service artificial intelligence system, and comprises the following steps:
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;
and 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, and uploading the suggested linkage interaction event to a corresponding block chain.
2. The application cloud service linkage big data processing method according to claim 1, wherein the generating of the interactive 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 scenario 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.
3. The application cloud service linkage big data processing method according to claim 2, 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.
4. The application cloud service linkage big data processing method according to claim 2, wherein the step of analyzing the interaction demand knowledge graph of the interaction demand data comprises:
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 interaction 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 with the connectivity of the cloud service activity information larger 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 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.
5. The method for processing the big data linked with the application cloud service according to claim 1, wherein the step of extracting the linkage knowledge node from the interaction requirement knowledge map to obtain a linkage knowledge node sequence, extracting the cloud service element from the interaction requirement knowledge map 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 the suggested linkage interaction event of the interaction requirement data, and uploading the linkage interaction event to a corresponding block chain comprises:
analyzing the knowledge entities with cross in the interaction required knowledge graph to obtain a linkage knowledge node sequence;
extracting cloud service equipment objects covered by each knowledge entity in the interaction requirement knowledge graph to obtain a cloud service element sequence;
and mining corresponding linkage interaction events from a preset linkage interaction event sequence based on the linkage knowledge node sequence and the cloud service element sequence to determine recommended linkage interaction events of the interaction demand data, and uploading the recommended linkage interaction events to corresponding block chains, wherein each preset linkage interaction event in the preset linkage interaction event sequence has a corresponding linkage knowledge node and a corresponding cloud service element, and the recommended linkage interaction events of the interaction demand data are obtained based on matching results by matching the linkage knowledge node sequence and the cloud service element sequence with the linkage knowledge nodes and the cloud service elements covered by each preset linkage interaction event in the preset linkage interaction event sequence.
6. The method for processing the big data linked with the application cloud service according to claim 1, wherein before the interactive demand variable is transmitted to a cloud service linked decision network satisfying network convergence requirements, the method further comprises:
acquiring an initialized neural network model, wherein the initialized neural network model comprises a decision model unit;
acquiring reference collection data, wherein the reference collection data comprises reference collection interaction demand data, reference linkage decision information and corresponding demand support;
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;
and 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.
7. The method for processing the application cloud service linkage big data according to claim 1, wherein the step of generating the linkage support degree based on the support degree and the analysis information comprises:
acquiring a first influence coefficient of the cloud service linkage decision network, and determining a first support degree of the interaction demand data based on the support degree and the weighting support degree of the first influence coefficient;
obtaining a metric value corresponding to the analysis information, obtaining a second influence coefficient of the linkage interaction demand information, determining a second support degree of the interaction demand data based on the metric value and the weighting support degree of the second influence coefficient, calculating an addition support degree of the first support degree and the second support degree, and obtaining the linkage support degree.
8. The application cloud service linkage big data processing method according to claim 1, further comprising:
based on a cloud service linkage behavior database generated by the target cloud service user group based on the suggested linkage interaction event of the interaction demand data, performing interest mining on an input target cloud service linkage event log based on a target interest mining model to obtain corresponding target cloud service scene interest point distribution;
collecting at least one candidate cloud service push data matched with any one target cloud service scene interest point in the target cloud service scene interest point distribution;
comparing the user group portraits according to the candidate cloud service pushing data and the user group portraits of the target cloud service user group to obtain portraits characteristic missing parts related to each service pushing article in the candidate cloud service pushing data;
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;
and 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 cloud service user group to the service pushing article based on the pushing configuration information.
9. The application cloud service linkage big data processing method of claim 8, wherein the adding of the auxiliary article content to the missing part of the portrait feature related to each service push article in the at least one candidate cloud service push data to obtain the auxiliary push article information related to each service push article comprises:
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 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;
wherein, the determining the pushing support degree related to each service pushing article in the candidate cloud service pushing data according to the hotspot distribution information related to each service pushing article in the candidate cloud service pushing data and the hotspot distribution information corresponding to the auxiliary pushing article information thereof 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;
the determining, according to the auxiliary push article information and the push support degree 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 cloud service user group on the service push article based on the push configuration information includes:
determining derived extension strength corresponding to the auxiliary push article information based on push support degrees related to the same service push article in the at least one candidate cloud business service push data;
determining a target number of derived extensions corresponding to the serving push article-related secondary push article information 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 pushing configuration information, and performing information pushing of the target cloud service user group on the service pushed article based on the pushing configuration information.
10. A cloud service artificial intelligence system, which is characterized by comprising 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 application cloud service linkage big data processing method according to any one of claims 1 to 9.
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| CN115310558A (en) * | 2022-09-15 | 2022-11-08 | 张宾 | Big data analysis method and AI analysis system for cloud service abnormity optimization |
| CN115618377A (en) * | 2022-09-27 | 2023-01-17 | 黄星 | Data secrecy processing method and system and cloud platform |
| CN116578334A (en) * | 2023-07-12 | 2023-08-11 | 苏州盈天地资讯科技有限公司 | User online dynamic docking method and system based on configuration |
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- 2022-03-09 CN CN202210226565.6A patent/CN114564566A/en not_active Withdrawn
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| CN115310558A (en) * | 2022-09-15 | 2022-11-08 | 张宾 | Big data analysis method and AI analysis system for cloud service abnormity optimization |
| CN115310558B (en) * | 2022-09-15 | 2023-04-18 | 北京亿欧网盟科技有限公司 | Big data analysis method and AI analysis system for cloud service abnormity optimization |
| CN115618377A (en) * | 2022-09-27 | 2023-01-17 | 黄星 | Data secrecy processing method and system and cloud platform |
| CN115618377B (en) * | 2022-09-27 | 2023-10-27 | 北京国联视讯信息技术股份有限公司 | Data security processing method, system and cloud platform |
| CN116578334A (en) * | 2023-07-12 | 2023-08-11 | 苏州盈天地资讯科技有限公司 | User online dynamic docking method and system based on configuration |
| CN116578334B (en) * | 2023-07-12 | 2023-09-22 | 苏州盈天地资讯科技有限公司 | User online dynamic docking method and system based on configuration |
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