CN112685007A

CN112685007A - Information cloud computing pushing method combined with big data portrait and digital content server

Info

Publication number: CN112685007A
Application number: CN202110000252.4A
Authority: CN
Inventors: 凌清华
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-01-02
Filing date: 2021-01-02
Publication date: 2021-04-20

Abstract

The embodiment of the application provides an information cloud computing pushing method and a digital content server combined with a big data image, and in consideration of the fact that the cloud computing pressure for computing business service association vectors of all entities in a whole business feedback knowledge graph is large and the requirement for efficient analysis pushing cannot be met, a second business feedback knowledge graph is constructed on the basis of a second business service object and an existing first business feedback knowledge graph, namely, part of graph content of the first business feedback knowledge graph involved in determining business service association information of the second business service object is determined, so that information details are increased and the cloud computing pressure is reduced, the cloud computing efficiency of the business service association information of the second business service object is higher, and the requirement for efficient analysis pushing is better met.

Description

Information cloud computing pushing method combined with big data portrait and digital content server

Technical Field

The application relates to the technical field of big data, in particular to an information cloud computing pushing method and a digital content server combined with big data images.

Background

In the process of generating the service portrait, a large amount of micro-service operation data generated by a plurality of service operation nodes in the software service can be associated, so that the overall service portrait of the service object in the software service is determined, and the service portrait can be used for various applications related to the service portrait, for example, hot spot information push related by using the service portrait. However, in the process of pushing information based on a service portrait, for a newly added business service object, the global data volume is generally adopted for calculation, which increases the cloud computing pressure, is difficult to meet the requirement of efficient analysis and pushing, and may cause delay of subsequent application updating.

Disclosure of Invention

In order to overcome at least the above-mentioned deficiencies in the prior art, an object of the present application is to provide an information cloud computing push method and a digital content server that combine a big data image, in consideration of that the cloud computing pressure for computing the business service association vectors of all entities in the whole business feedback knowledge graph is large and cannot meet the requirement of efficient analysis push, a second business feedback knowledge graph is constructed based on a second business service object and an existing first business feedback knowledge graph, that is, a part of graph content of the first business feedback knowledge graph involved in determining the business service association information of the second business service object is used to increase information details and reduce the cloud computing pressure, so that the cloud computing efficiency of the business service association information of the second business service object is higher, and the requirement of efficient analysis push is better met.

In a first aspect, the present application provides an information cloud computing push method combining big data images, which is applied to a digital content server, where the digital content server is in communication connection with a plurality of mobile software servers, and the digital content server is implemented according to a cloud computing platform, and the method includes:

sending push hotspot information to a mobile software server corresponding to a service object according to an integral service portrait of the service object in the software service;

acquiring push hotspot feedback behavior information fed back by the mobile software server aiming at the push hotspot information, and acquiring a first service feedback knowledge graph from the push hotspot feedback behavior information, wherein one entity in the first service feedback knowledge graph corresponds to a first service object, and entity attributes among the entities in the first service feedback knowledge graph are used for expressing service feedback interaction behaviors among the first service objects;

constructing a second business feedback knowledge graph according to business service update information of a newly added second business service object and the first business feedback knowledge graph, wherein at least two entities in the second business feedback knowledge graph are respectively used for representing the second business service object and at least one first business service object having business feedback interaction behavior with the second business service object, and at least one entity attribute between the entities in the second business feedback knowledge graph is respectively used for representing the business feedback interaction behavior between the second business service object and the at least one first business service object;

and determining business service associated information of the second business service object according to the second business feedback knowledge graph, so as to perform information push on the newly added second business service object based on the business service associated information, wherein the business service associated information is used for representing portrait updating characteristics of the second business service object and feedback interaction behavior portrait characteristics between the second business service object and the at least one first business service object.

In a possible example design of the first aspect, the step of constructing a second business feedback knowledge graph according to business service update information of a newly added second business service object and the first business feedback knowledge graph includes:

determining a target service feedback interaction behavior from at least one service feedback interaction behavior indicated by the first service feedback knowledge map according to the software micro-service label of the first service feedback knowledge map;

determining at least one first business service object having the target business feedback interaction behavior with the second business service object according to business service update information of the second business service object and the first business feedback knowledge graph;

and constructing a second business feedback knowledge graph according to the target business feedback interaction behavior, the second business service object and the at least one first business service object.

In one possible example design of the first aspect, the target service feedback interaction behavior is a direct interaction behavior and an indirect interaction behavior;

the step of determining at least one first business service object having the target business feedback interaction behavior with the second business service object according to the business service update information of the second business service object and the first business feedback knowledge graph comprises:

and determining at least one first business service object having the direct interaction behavior with the second business service object and at least one first business service object having the indirect interaction behavior with the second business service object according to the business service update information of the second business service object and the first business feedback knowledge graph.

In a possible example design of the first aspect, the step of constructing a second service feedback knowledge graph according to the target service feedback interaction behavior includes:

constructing a node of a second business feedback knowledge graph according to the second business service object and the at least one first business service object, wherein one entity corresponds to one business service object;

and constructing entity attributes among the entities according to the target service feedback interaction behavior to obtain a second service feedback knowledge graph.

In a possible example design of the first aspect, after the step of constructing a second service feedback knowledge-graph according to the target service feedback interaction behavior, the method further includes:

determining first interaction strength after non-interactive entities in the second service feedback knowledge graph become interactive entities one by one according to hierarchical division;

and in response to the first interaction strength being greater than a second interaction strength, replacing the non-interactive entity with an interactive entity, wherein the second interaction strength is the interaction strength of the second service feedback knowledge graph before the non-interactive entity becomes the interactive entity.

In a possible example design of the first aspect, the step of determining business service association information of the second business service object according to the second business feedback knowledge-graph includes:

performing feature extraction on the second service feedback knowledge graph, and determining feedback interaction behavior portrait features of the service feedback knowledge graph between service objects corresponding to at least two entities in the second service feedback knowledge graph;

respectively extracting the characteristics of the service updating information of the service objects corresponding to at least two entities in the second service feedback knowledge graph to obtain the portrait updating characteristics of the service feedback knowledge graph;

and determining business service associated information of the second business service object according to the feedback interaction behavior portrait characteristics of the business feedback knowledge map and the portrait updating characteristics of the business feedback knowledge map.

In a possible example design of the first aspect, the step of determining business service associated information of the second business service object according to the feedback interaction behavior profile feature of the business feedback knowledge graph and the profile updating feature of the business feedback knowledge graph includes:

obtaining a plurality of service portrait learning interfaces determined according to a plurality of entities in the first service feedback knowledge graph, wherein the service portrait learning interfaces are used for calculating a group of service portrait information and returning a calculation result;

acquiring a business service description model, and taking the plurality of business portrait learning interfaces as parameters of the business service description model;

fusing feedback interaction behavior portrait characteristics of the service feedback knowledge map and portrait updating characteristics of the service feedback knowledge map to obtain fusion characteristics;

and extracting the service description feature of the fusion feature based on the service description model, determining the obtained description feature as a service association vector of the second service object, and taking the service association vector as service association information of the second service object.

In a second aspect, an embodiment of the present application further provides an information cloud computing pushing apparatus combining a big data image, which is applied to a digital content server, where the digital content server is in communication connection with a plurality of mobile software servers, and the digital content server is implemented according to a cloud computing platform, and the apparatus includes:

the sending module is used for sending push hotspot information to a mobile software server corresponding to a service object according to an integral service portrait of the service object in the software service;

an obtaining module, configured to obtain hotspot feedback behavior information fed back by the mobile software server for the hotspot information, and obtain a first service feedback knowledge graph from the hotspot feedback behavior information, where one entity in the first service feedback knowledge graph corresponds to a first service object, and an entity attribute between entities in the first service feedback knowledge graph is used to represent a service feedback interaction behavior between the first service objects;

the building module is used for building a second business feedback knowledge graph according to business service updating information of a newly added second business service object and the first business feedback knowledge graph, wherein at least two entities in the second business feedback knowledge graph are respectively used for representing the second business service object and at least one first business service object having business feedback interaction behaviors with the second business service object, and at least one entity attribute between the entities in the second business feedback knowledge graph is respectively used for representing the business feedback interaction behaviors between the second business service object and the at least one first business service object;

and the pushing module is used for determining business service associated information of the second business service object according to the second business feedback knowledge graph so as to push information of the newly added second business service object based on the business service associated information, wherein the business service associated information is used for representing portrait updating characteristics of the second business service object and feedback interaction behavior portrait characteristics between the second business service object and the at least one first business service object.

In a third aspect, an embodiment of the present application further provides an information cloud computing push system combined with a big data portrait, where the information cloud computing push system combined with a big data portrait includes a digital content server and a plurality of mobile software servers in communication connection with the digital content server;

the digital content server is configured to:

In a fourth aspect, an embodiment of the present application further provides a digital content server, where the digital content server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be in communication connection with at least one mobile software server, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium, so as to execute the method for pushing information cloud computing in combination with a large data representation in the first aspect or any one of the possible design examples in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform an information cloud computing pushing method in combination with a big data representation in the first aspect or any one of the possible design examples of the first aspect.

According to any one of the aspects, in consideration of the fact that the cloud computing pressure for computing the business service association vectors of all entities in the whole business feedback knowledge graph is large and the requirement for efficient analysis and pushing cannot be met, the second business feedback knowledge graph is constructed on the basis of the second business service object and the existing first business feedback knowledge graph, namely, the information details are increased and the cloud computing pressure is reduced by determining the content of part of the graph of the first business feedback knowledge graph involved in the business service association information of the second business service object, so that the cloud computing efficiency of the business service association information of the second business service object is higher, and the requirement for efficient analysis and pushing is better met.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic view of an application scenario of an information cloud computing push system incorporating a big data portrait according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating an information cloud computing pushing method combined with a big data portrait according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of functional modules of an information cloud computing pushing device incorporating a big data portrait according to an embodiment of the present disclosure;

fig. 4 is a schematic block diagram of structural components of a digital content server for implementing the above information cloud computing pushing method in combination with a large data representation according to an embodiment of the present application.

Detailed Description

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

FIG. 1 is an interaction diagram of an information cloud computing push system 10 incorporating a big data portrait according to an embodiment of the present application. The information cloud computing push system 10 combined with the big data portrait can comprise a digital content server 100 and a mobile software server 200 which is in communication connection with the digital content server 100. The information cloud computing push system 10 incorporating a large data representation shown in FIG. 1 is but one possible example, and in other possible embodiments, the information cloud computing push system 10 incorporating a large data representation may also include only at least some of the components shown in FIG. 1 or may also include other components.

According to the invention concept of the technical solution provided by the present application, the digital content server 100 provided by the present application may be applied to a scenario where a big data technology or a cloud computing technology may be applied, such as smart medical, smart city management, smart industrial internet, and general service monitoring management, and for example, may also be applied to a new energy vehicle system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud vehicle management platform, block chain financial service platform, and the like, but is not limited thereto.

In this embodiment, the digital content server 100 and the mobile software server 200 in the information cloud computing push system 10 combined with a big data portrait may cooperatively perform the information cloud computing push method combined with a big data portrait described in the following method embodiments, and for the specific steps of the digital content server 100 and the mobile software server 200, reference may be made to the detailed description of the following method embodiments.

To solve the technical problem in the foregoing background, fig. 2 is a schematic flow chart of an information cloud computing push method combined with a large data portrait according to an embodiment of the present application, which can be executed by the digital content server 100 shown in fig. 1, and the information cloud computing push method combined with a large data portrait is described in detail below.

Step S110, sending push hotspot information to the mobile software server 200 corresponding to the service object according to the overall service representation of the service object in the software service.

Step S120, obtaining the push hotspot feedback behavior information fed back by the mobile software server 200 for the push hotspot information, and obtaining the first service feedback knowledge graph from the push hotspot feedback behavior information.

In this embodiment, one entity in the first service feedback knowledge graph corresponds to one first service object, and an entity attribute between entities in the first service feedback knowledge graph is used to represent a service feedback interaction behavior between the first service objects.

In this embodiment, the service feedback interaction behavior may represent various feedback interaction behaviors, such as a direct interaction behavior (for example, a feedback interaction that a service object directly performs a positive/negative keyword tendency for a certain push hotspot) or an indirect interaction behavior (for example, a feedback interaction that a third party or another channel performs a positive/negative keyword tendency for a certain push hotspot between service objects), and the like, which is not limited in the embodiments of the present application. The business service object may be understood as a specific user, such as a personal user, an enterprise user, a community user, and the like, but is not limited thereto.

Step S130, a second service feedback knowledge map is constructed according to the service update information of the second service object and the first service feedback knowledge map.

In this embodiment, at least two entities in the second service feedback knowledgegraph are respectively used to represent the second service object and at least one first service object having a service feedback interaction behavior with the second service object, and at least one entity attribute between the entities in the second service feedback knowledgegraph is respectively used to represent the service feedback interaction behavior between the second service object and the at least one first service object.

In one possible design example, after the first business feedback knowledge graph is obtained, the first business service object having business feedback interaction behavior with the second business service object can be obtained from the first business feedback knowledge graph according to business service update information of the second business service object, and then the second business feedback knowledge graph is constructed by taking the second business service object and the obtained first business service object as entities and taking business feedback interaction behavior between the entities as entity attributes. That is, the second service feedback knowledgegraph includes a plurality of entities having service feedback interaction behaviors, and when a new entity needs to be added to the first service feedback knowledgegraph, if a second service object is added, a second service feedback knowledgegraph can be constructed according to the service feedback interaction behaviors between the second service object added newly and the first service object corresponding to the entity in the first service feedback knowledgegraph, where the second service feedback knowledgegraph is a part of graph content of the first service feedback knowledgegraph.

Step S140, determining service associated information of the second service object according to the second service feedback knowledge graph, so as to perform information pushing on the newly added second service object based on the service associated information, where the service associated information is used to represent a portrait update feature of the second service object and a feedback interaction behavior portrait feature between the second service object and the at least one first service object.

In a possible design example, after the second service feedback knowledge graph is constructed, feature extraction can be performed on the second service feedback knowledge graph to obtain feedback interaction behavior portrait features of the service feedback knowledge graph among service objects corresponding to each entity, and feature extraction can be performed on the service objects corresponding to each entity to obtain portrait updating features of the service feedback knowledge graph. And processing feedback interaction behavior portrait characteristics of the business feedback knowledge graph and portrait updating characteristics of the business feedback knowledge graph based on a business service description model to obtain a business service association vector of the second business service object, and determining the business service association vector as business service association information of the second business service object.

It should be noted that the business service description model may be, but is not limited to, an induction learning algorithm, so that the business service description model may be used to calculate the business service association vector of the second business service object without retraining, thereby obtaining the business service association information of the second business service object.

In a possible design example, in the process of pushing information to the newly added second business service object based on the business service related information, the corresponding hotspot push data source may be matched based on the keyword associated with the portrait feature in the business service related information, and the matched content may be sent to the mobile software server 200 as push content.

Based on the above steps, in this embodiment, in consideration of the fact that the cloud computing pressure for computing the business service association vectors of all entities in the whole business feedback knowledge graph is large and cannot meet the requirement of efficient analysis and pushing, the second business feedback knowledge graph is constructed based on the second business service object and the existing first business feedback knowledge graph, that is, the information details are increased and the cloud computing pressure is reduced by determining the content of a part of the graph of the first business feedback knowledge graph involved in the business service association information of the second business service object, so that the cloud computing efficiency of the business service association information of the second business service object is higher, and the requirement of efficient analysis and pushing is better met.

In one possible design example, the aforementioned portrait update feature determination method based on the business feedback knowledge graph may be implemented by the following exemplary embodiments.

In one possible design example, the business service update information of the second business service object may be data that the second business service object has performed business service update, including update attribute information of the second business service object, such as, but not limited to, a service object name, a service object age, a service object occupation, a service object workspace, and a service object registration tag, a service object service interaction tag, a service object service evaluation tag, and the like. After acquiring the first service feedback knowledge graph, the digital content server 100 may acquire, according to service update information of the second service object, the first service object having a service feedback interaction behavior with the second service object from the first service feedback knowledge graph, and then construct the second service feedback knowledge graph with the second service object and the acquired first service object as entities and the service feedback interaction behavior between the service objects as an entity attribute. That is, the second business feedback knowledge graph includes a plurality of entities having business feedback interaction behaviors, and one entity corresponds to one business service object, that is, one entity corresponds to one first business service object or corresponds to the second business service object. When a newly registered service object exists, a new entity needs to be added to the first service feedback knowledge graph, and then the digital content server 100 can construct a second service feedback knowledge graph according to a service feedback interaction behavior between the newly added second service object and the first service object corresponding to the entity in the first service feedback knowledge graph, where the second service feedback knowledge graph is a part of graph content of the first service feedback knowledge graph.

In one possible design example, the business feedback interaction behavior is associated with a software microservice tag of a first business feedback knowledge-graph. Accordingly, the step of constructing the second service feedback knowledge graph by the digital content server 100 according to the service update information of the second service object and the first service feedback knowledge graph may be: the digital content server 100 determines a target service feedback interaction behavior from at least one service feedback interaction behavior indicated by the first service feedback knowledge graph according to the software microservice tag of the first service feedback knowledge graph. Then, the digital content server 100 determines at least one first business service object having the target business feedback interaction behavior with the second business service object according to the business service update information of the second business service object and the first business feedback knowledge graph. Finally, the digital content server 100 constructs a second service feedback knowledge graph according to the target service feedback interaction behavior, the second service object and the at least one first service object. At least one service feedback interaction behavior, such as a direct interaction behavior, an indirect interaction behavior and the like, can be provided between the first service objects corresponding to the entities in the first service feedback knowledge graph, and different service feedback interaction behaviors can have software micro-service tags of different levels. The target service feedback interaction behavior for constructing the second service feedback knowledge graph is determined according to the software micro-service label of the first service feedback knowledge graph, so that the constructed second service feedback knowledge graph can better accord with the graph class details and the software micro-service label of the first service feedback knowledge graph.

In one possible design example, the target business feedback interaction behavior is a direct interaction behavior and an indirect interaction behavior, where the indirect interaction behavior refers to that if one business service object does not have a direct interaction behavior with another business service object but has a direct interaction behavior with the same business service object, the two business service objects are indirect interaction behaviors, and in short, a focus service object of one business service object, or a focus service object of an interaction, or a subscription, or a browsing service object, has an indirect interaction behavior with the business service object. Accordingly, the digital content server 100 may update information and the first business feedback knowledgegraph according to the business service of the second business service object. On this basis, the step of determining at least one first business service object having a target business feedback interaction behavior with the second business service object may be: the digital content server 100 can determine at least one first business service object having a direct interaction behavior with the second business service object and at least one first business service object having an indirect interaction behavior with the second business service object according to the business service update information of the second business service object and the first business feedback knowledge graph.

For example, taking a software microservice tag of a first business feedback knowledge graph as an e-commerce live microservice tag as an example, the digital content server 100 can obtain target business service object identifiers of a plurality of business service objects establishing direct interaction behavior with a second business service object according to the e-commerce live microservice tag and business service update information of the second business service object, then determine at least one first business service object having the target business service object identifier from first business service objects corresponding to entities in a first business feedback knowledge graph according to the target business service object identifiers of the plurality of business service objects, then obtain at least one first business service object having direct interaction behavior with the at least one first business service object according to the first business feedback knowledge graph, and obtain at least one first business service pair having indirect interaction behavior with the second business service object Such as a mouse.

In one possible design example, the step of the digital content server 100 constructing the second service feedback knowledge graph according to the target service feedback interaction behavior is as follows: the digital content server 100 constructs entities of the second service feedback knowledge graph according to the second service object and at least one first service object, wherein one entity corresponds to one service object, that is, one entity corresponds to the second service object or the first service object. Then, the digital content server 100 constructs entity attributes between entities according to the target service feedback interaction behavior, and obtains a second service feedback knowledge graph.

Next, the digital content server 100 may also perform an optimization process on the second service feedback knowledge graph.

In one possible design example, after the digital content server 100 constructs the second business feedback knowledge graph, the second business feedback knowledge graph can be further processed to reduce the number of entities and entity attributes in the second business feedback knowledge graph, so as to reduce the cloud computing pressure when determining the business service association information of the second business service object, i.e., when computing the business service association vector. In one possible design example, the digital content server 100 can optimize the second service feedback knowledge graph constructed in the above steps in different ways according to different algorithms used in calculating the service association vector. For example, optimization processing is performed in a random sampling manner, which is not limited in the embodiment of the present application.

In a possible design example, the step of the digital content server 100 performing optimization processing on the second service feedback knowledge graph may be: the digital content server 100 determines the first interaction strength after the non-interactive entities in the second service feedback knowledge-graph become interactive entities one by one according to hierarchical division. For any non-interactive entity, in response to the first interaction intensity corresponding to the non-interactive entity being greater than the second interaction intensity, replacing the non-interactive entity with an interactive entity. Wherein the second interaction strength is the interaction strength of the second service feedback knowledge graph before the non-interactive entity becomes the interactive entity. By replacing the non-interactive entity with the interactive entity, the number of entities and entity attributes in the second service feedback knowledge graph is reduced on the premise of not reducing the performance of the service feedback knowledge graph, so that the cloud computing pressure is reduced, and the computing efficiency is improved.

In a possible design example, the digital content server 100 may further optimize the second service feedback knowledge graph according to the software micro-service tag, so as to improve the construction efficiency of the second service feedback knowledge graph, where, if the target service feedback interaction behavior is a two-layer interaction branching relationship, such as a direct interaction behavior and an indirect interaction behavior, the optimization manner is not limited in the embodiment of the present application.

For example, taking the graph sage algorithm as an example, for the application of the graph sage algorithm to the live broadcast e-commerce micro service tag, the second business feedback knowledge graph can be constructed by using the service object concerned by the business service object and the service object concerned by the business service object, so as to improve the efficiency of constructing the second business feedback knowledge graph.

On the basis, the digital content server 100 determines, according to the second service feedback knowledge graph, service-related information of the second service object, where the service-related information is used to represent a representation updating feature of the second service object and a feedback interaction behavior representation feature between the second service object and the at least one first service object.

In a possible design example, after the digital content server 100 constructs the second service feedback knowledge graph, the topology of the second service feedback knowledge graph may be obtained, and the digital content server 100 may perform feature extraction on the second service feedback knowledge graph based on the topology to determine the feedback interaction behavior profile features of the service feedback knowledge graph between service objects corresponding to at least two entities in the second service feedback knowledge graph. Then, the digital content server 100 can further perform feature extraction on the service update information corresponding to at least two entities in the second service feedback knowledge graph, respectively, to obtain the portrait update features of the service feedback knowledge graph. The digital content server 100 determines the service associated information of the second service object according to the feedback interaction behavior profile characteristic of the service feedback knowledge graph and the profile updating characteristic of the service feedback knowledge graph. By respectively extracting the characteristics of the service updating information and the second service feedback knowledge map, the obtained service associated information can represent the portrait updating characteristics of the second service object and the feedback interaction behavior portrait characteristics between the second service object and the first service object, so that the service associated vector of the second service object can be more accurately determined based on the service associated information.

It should be noted that, after obtaining the feedback interaction behavior profile features of the service feedback knowledge graph and the service object of the service feedback knowledge graph, the digital content server 100 may also perform preprocessing on the above features, such as data cleaning, removing unnecessary features and error features, and then perform normalization processing on the features according to the feature types, so as to facilitate subsequent calculation. The embodiment of the present application does not limit this.

In one possible design example, the digital content server 100 can determine a plurality of business representation learning interfaces from a plurality of entities in the first business feedback knowledge graph, which can be used to compute business service association vectors for newly joined entities.

Accordingly, the digital content server 100 can obtain a plurality of service portrait learning interfaces determined according to a plurality of entities in the first service feedback knowledge graph when determining the service associated information of the second service object, where the service portrait learning interfaces are used for calculating a set of service portrait information and returning a calculation result.

Then, the digital content server 100 calculates a service association vector according to the plurality of service representation learning interfaces, the feedback interaction behavior representation feature of the service feedback knowledge graph, and the representation updating feature of the service feedback knowledge graph, determines a service association vector of the second service object, and uses the service association vector as service association information of the second service object.

By obtaining the feedback interaction behavior portrait characteristics of the service feedback knowledge map and the portrait updating characteristics of the service feedback knowledge map, the digital content server 100 can calculate the service association vector of the second service object based on the service portrait learning interface corresponding to other entities, so as to more accurately determine the service association vector of the second service object.

For example, the GraphSAGE algorithm can perform two-layer learning on each entity in the first service feedback knowledge graph, generate a series of service portrait learning interfaces according to the obtained characteristics, wherein each entity corresponds to one service portrait learning interface, and obtain the description characteristic vector of the second service object through the service portrait learning interface, the portrait updating characteristics of the service feedback knowledge graph and the feedback interaction behavior portrait characteristics of the service feedback knowledge graph.

In one possible design example, the digital content server 100 can obtain a business service description model, use the plurality of business portrait learning interfaces as parameters of the business service description model, process feedback interaction behavior portrait characteristics of the business feedback knowledge graph and portrait update characteristics of the business feedback knowledge graph based on the business service description model, and determine a business service association vector of the second business service object. The business service description model is constructed based on an inductive learning class diagram algorithm.

The digital content server 100 can fuse the feedback interaction behavior portrait characteristics of the service feedback knowledge graph and the portrait update characteristics of the service feedback knowledge graph to obtain fusion characteristics. And then, extracting the service description feature of the fusion feature based on the service description model, and determining the obtained description feature as a service association vector of a second service object.

Because the business service description model is constructed based on the induction learning class diagram algorithm, when a new entity is added into the first business feedback knowledge graph, the business service association vector of the business service object corresponding to the newly added entity can be determined without retraining the business service description model.

It should be noted that the service association vector of the first service object corresponding to each entity in the first service feedback knowledgebase can be called by at least one push network model, and the at least one push network model is used for implementing at least one online push service, and is not limited specifically. Wherein the at least one online model can be referred to as a downstream application of the first business feedback knowledge graph, in one possible design example, the at least one online model is a machine learning model, and is not particularly limited.

In one possible design example, the foregoing step S110 may be implemented by the following substeps.

And step S111, determining a first unit service portrait corresponding to each software microservice in the plurality of software microservices based on microservice operation data of a service object sent by service operation nodes in the plurality of software microservices through a plurality of portrait classification network models respectively, wherein one portrait classification network model is used for determining a corresponding first unit service portrait based on microservice operation data of the service object in one software microservice, and the plurality of software microservices are obtained by carrying out service differentiation on the software service.

For example, in this embodiment, the divided software microservices may be directly used.

The micro service operation data may include a plurality of service data fragments, where the plurality of service data fragments are service data fragments of a service object in a micro service program of a service operation node that sends the micro service operation data, that is, the service operation node performs service operation record on the service object in the micro service program, determines micro service operation data of the service object, and sends the micro service operation data to the node.

In one possible design example, each service data fragment may correspond to service location node information and a service object intention feature of a corresponding service object, the target intention feature may include an overall intention feature and a local intention feature of the service object, and the target intention feature is obtained by intention identification of the service object through a corresponding service operation node.

The representation classification network model is a program unit of a neural network engine in the digital content server 100, and is used for performing correlation calculation on microservice operation data in a software microservice to obtain a first unit service representation of a service object in the software microservice.

In one possible design example, the digital content server 100 may receive micro-service operation data for a service object sent by a plurality of service operation nodes within a software service, the micro service operation data may include service operation node labels of service operation nodes, and then the digital content server 100 sends the micro service operation data of the service object sent by the plurality of service operation nodes to the corresponding portrait classification network models respectively according to the pre-stored service operation node labels of the service operation nodes included in each software micro service, sends the micro service operation data sent by the service operation nodes included in the same software micro service to the same portrait classification network model, and then determining a first unit service portrait corresponding to the software microservice by a portrait classification network model based on the acquired microservice operation data of the service object in the software microservice.

In one possible design example, determining, by the plurality of profile classification network models, a specific implementation of a first unit service profile corresponding to each of the plurality of software microservices based on microservice operation data of the service object sent by the service operation node within the plurality of software microservices, respectively, may include: for a first portrait classification network model in the portrait classification network models, a second unit service portrait corresponding to a first software micro-service determined by a previous portrait acquisition node is acquired through the first portrait classification network model, the first portrait classification network model is any portrait classification network model in the portrait classification network models, and the first software micro-service is one software micro-service in the software micro-services. And determining a first unit service portrait corresponding to the first software microservice based on the acquired second unit service portrait and microservice operation data of a service object in the first software microservice through the first portrait classification network model.

The portrait acquisition node refers to a duration time period or a random trigger time period. In one possible design example, the service images of the service objects are periodically associated, one image acquisition node is a image period, and the micro-service operation data of the service objects sent by the service operation node is acquired every other image acquisition node. For example, the sketch acquiring node may be a day, that is, the service operation node reports the microservice operation data determined in the day every day.

The number of the second unit service drawings may be at least one, and each second unit service drawing may correspondingly include a plurality of service data fragments.

That is, for each of the plurality of software microservices, the first unit service representation corresponding to each of the software microservices may be determined in the manner described above. Taking the first software microservice as an example, in one software microservice, the service images of the same service object at different image acquisition nodes need to be associated, that is, the service images of the same service object at different image acquisition nodes are associated with the same microservice object label, more specifically, the microservice object label of the same service object is continued from the second unit service image of the previous image acquisition node to the microservice operation data of the next image acquisition node, wherein the microservice object label is used for uniquely identifying one service object in the software microservice. Therefore, a second unit service representation corresponding to the first software microservice determined by a previous representation obtaining node is obtained through a first network unit corresponding to the first software microservice, and then a first unit service representation corresponding to the first software microservice is determined according to the second unit service representation and obtained microservice operation data of a service object sent by at least one service operation node in the first software microservice.

In one possible design example, the second unit service representation and the micro-service operation data of the service object in the first software micro-service can be directly associated to obtain the first unit service representation corresponding to the first software micro-service.

In one possible design example, for each micro service operation data of the service object in the first software micro service, the service portrait corresponding to each micro service operation data may be compared with the second unit service portrait, the matching degree between the service portrait corresponding to each micro service operation data and the second unit service portrait is determined, and if the second unit service portrait with the matching degree larger than the matching degree threshold exists in the service portrait corresponding to each micro service operation data, the service portrait corresponding to each micro service operation data is associated with the second unit service portrait with the corresponding matching degree larger than the matching degree threshold, so as to obtain the first unit service portrait corresponding to the first software micro service.

The matching degree threshold may be set by a user according to actual needs, or may be set by default by the digital content server 100, which is not limited in the embodiment of the present application. For example, the threshold of the degree of match may be 0.75.

In a possible design example, taking reference micro service operation data of a service object in a first software micro service as an example, a matching degree between a service portrait corresponding to the reference micro service operation data and each second unit service portrait may be determined, and if there is a second unit service portrait whose matching degree is greater than a threshold value of the matching degree, a micro service object tag corresponding to the second unit service portrait whose matching degree is greater than the threshold value of the matching degree is determined as the micro service object tag of the reference micro service operation data.

The reference micro-service operation data is one micro-service operation data in the micro-service operation data of the service object in the first software micro-service.

Exemplarily, it is assumed that a first software microservice in a previous portrait acquisition node includes two second unit service images, corresponding microservice object labels of the two second unit service images are a and B, respectively, and a first software microservice in a current portrait acquisition node includes two microservice operation data, which are denoted as microservice operation data C and microservice operation data D. For the micro-service operation data C, the matching degree of the micro-service operation data C and the service portrait corresponding to the A and the matching degree of the micro-service operation data C and the service portrait corresponding to the B can be determined, if the matching degree of the micro-service operation data C and the service portrait corresponding to the A is greater than the threshold value of the matching degree, the service portrait corresponding to the micro-service operation data C and the A can be determined to be the service portrait of the same service object, and the micro-service object label of the micro-service operation data C can be determined to be A. Similarly, for the micro-service operation data D, the matching degree of the micro-service operation data D and the service image corresponding to a and the matching degree of the micro-service operation data D and the service image corresponding to B may be determined, if the matching degree of the micro-service operation data D and the service image corresponding to B is greater than the matching degree threshold, it may be determined that the service images corresponding to the micro-service operation data D and B are the service image of the same service object, and the micro-service object label of the micro-service operation data D may be determined as B. In this manner, the objective of associating microservice operational data C and D with a second unit service representation is achieved.

For example, in one possible design example, the determining the matching degree between the service image corresponding to the reference microservice operation data and the reference second unit service image may include: and determining a first matching degree of the target intention characteristic in the reference micro-service operation data and the intention characteristic of the service object of the reference second unit service image, and a second matching degree of the data characteristic of the service data fragment farthest from the current time in the reference micro-service operation data and the data characteristic of the service data fragment nearest to the current time in the reference second unit service image, weighting, summing and averaging the first matching degree and the second matching degree to obtain the matching degree of the service image corresponding to the reference micro-service operation data and the reference second unit service image.

Further, if there is no second unit service representation with a matching degree greater than the threshold matching degree, a new micro service object tag may be determined for the tag corresponding to the reference micro service operation data.

In other embodiments, determining, by the first image classification network model, a specific implementation of the first unit service representation corresponding to the first software microservice based on the obtained second unit service image and microservice operational data of the service object within the first software microservice may include: and if the obtained second unit service portrait is determined to have unprocessed service operation data through the first portrait classification network model, associating the micro service operation data of the service object in the first software micro service with the unprocessed service operation data, wherein the unprocessed service operation data refers to the second unit service portrait of which the portrait classification time of the micro service operation data corresponding to the tail service data fragment in the included service data fragments is greater than the portrait classification time threshold. And carrying out portrait classification on the associated service operation data through a first portrait classification network model so as to determine a first unit service portrait corresponding to the first software microservice.

The portrait classification time threshold may be set by a user according to actual needs, or may be set by default by the digital content server 100, which is not limited in this embodiment.

In this case, the portrait classification time of the micro service operation data corresponding to the last service data fragment in each second unit service portrait refers to a portrait classification time of the micro service operation data corresponding to the last service data fragment in the second unit service portrait, that is, a portrait classification time of the micro service operation data of the last micro service operation data included in the second unit service portrait.

Taking a picture obtaining node of 24 hours a day as an example, if a service picture of a service object in a software micro service is not finished, the service object in the software micro service always has the service picture in a picture obtaining node, if the picture classification time of the micro service operation data of the service object is 20 hours, it indicates that the service object may not update the service picture in the software micro service in the last 4 hours, the service object may be considered to have left the software micro service, therefore, the micro service operation data of 20 hours of the service object can be determined as the finished service picture, but if the picture classification time of the micro service operation data of the service object is 23 hours, it indicates that no service picture may exist in 1 hour, but the time of 1 hour is very short, since erroneous determination is likely to occur, the service target micro-service operation data of 23 hours is determined as unprocessed service operation data.

In this implementation, since the service object corresponding to the service image that has been completed in the previous image acquisition node may have left the first software microservice, the microservice operation data of the service object does not exist in the current image acquisition node, that is, the service image corresponding to the microservice operation data of the service object in the current image acquisition node is not associated with the completed service image, and if all the acquired second unit service images are associated with the microservice operation data of the service object, the cloud computing pressure of the image classification network model is increased, and the time for determining the first unit service image is further increased. Therefore, the unprocessed service operation data in the acquired second unit service image can be determined, and then the unprocessed service operation data is associated with the micro-service operation data of the service object in the first software micro-service, so as to obtain the first unit service image corresponding to the first software micro-service.

In one possible design example, after determining that the unprocessed service operation data exists, a specific implementation of associating the micro-service operation data of the service object in the first software micro-service with the unprocessed service operation data may include: for each micro service operation data in the micro service operation data of the service object in the first software micro service, comparing each micro service operation data with unprocessed service operation data, determining the matching degree of each micro service operation data and the unprocessed service operation data, and if the unprocessed service operation data with the matching degree larger than the matching degree threshold exists in each micro service operation data, associating each micro service operation data with the unprocessed service operation data with the corresponding matching degree larger than the matching degree threshold.

For example, taking reference micro service operation data of a service object in the first software micro service as an example, a matching degree between a service portrait corresponding to the reference micro service operation data and each unprocessed service operation data may be determined, and if there is unprocessed service operation data whose matching degree is greater than a matching degree threshold, a micro service object tag corresponding to the unprocessed service operation data whose matching degree is greater than the matching degree threshold is determined as a micro service object tag of the reference micro service operation data.

It should be noted that, the specific implementation manner for determining the matching degree refers to the related description of the above embodiment, and this embodiment is not described herein again.

After the micro service object label of the micro service operation data of each service object in the first software micro service is determined, the associated service operation data can be subjected to portrait classification through the first portrait classification network model so as to determine a first unit service portrait corresponding to the first software micro service.

In one possible design example, after determining a first unit service representation corresponding to a first software microservice, unprocessed service operation data in the first unit service representation may be determined and then stored in an unprocessed service operation data cache list.

Step S112, based on the first unit service portrait corresponding to each software microservice in the plurality of software microservices, determining the whole service portrait of the service object in the software business service.

In one possible design example, after the first unit service representation corresponding to each software microservice in the current representation acquisition node is determined, service representation association between the software microservices can be performed to determine an overall service representation of a service object in the software business service.

In one possible design example, determining a particular implementation of an overall service representation of a service object within a software business service based on a first unit service representation corresponding to each of a plurality of software microservices may include: and for each software microservice in the plurality of software microservices, if other software microservices which have associated business program interfaces with each software microservice exist in the software business service, acquiring an integral service portrait of a service object in the software business service determined by the previous portrait acquisition node, and acquiring a past integral service portrait. If the service portrait corresponding to other software micro services of the associated service program interface of each software micro service is included in the past integral service portrait, and the service portrait of the same service object belongs to the first unit service portrait corresponding to each software micro service is included in the past integral service portrait, the first unit service portrait corresponding to each software micro service is associated with the service portrait belonging to the same service object in the past integral service portrait. And associating the service portrait after each software microservice is associated to obtain an integral service portrait of a service object in the software business service.

That is, for each software microservice, it may be determined whether there is another software microservice having a business program interface associated with each software microservice in the software business service, and if so, an overall service representation of the service object in the software business service determined in the previous representation obtaining node may be obtained. For convenience of description, the whole service image of the service object in the previous image acquisition node is referred to as the past whole service image.

Judging whether the service portrait corresponding to other software micro-services of which each software micro-service has a related service program interface exists in the whole service portrait, if so, judging whether the whole service portrait in the past comprises a service portrait of which the first unit service portrait corresponding to each software micro-service belongs to the same service object, for convenience of description, a service image in which a first unit service image corresponding to each software microservice belongs to the same service object is referred to as a same-target service image, and if a whole service image includes the same-target service image corresponding to each software microservice, the first unit service representation corresponding to each software microservice can be associated with the same target service representation corresponding to each software microservice, and the service representations associated with each software microservice can be associated with each other to obtain an overall service representation of a service object in the software service.

In one possible design example, taking the first software microservice as an example, it can be determined through the first image classification network model whether there are other software microservices having associated business program interfaces with the first software microservice, if so, the digital content server 100 can obtain an overall service image of the service object in the software business service determined by the previous image obtaining node to obtain a past overall service image, then determine whether the past overall service image includes the service image corresponding to the other software microservices having associated business program interfaces with the first software microservice, if so, determine whether the past overall service image includes the service image of the same service object as the first unit service image corresponding to the first software microservice, if so, associate the first unit image corresponding to the first software microservice with the service image of the same service object as the past overall service image, and obtaining a service portrait after the first software micro-service is associated.

In one possible design example, the presence of other software microservices within a software business service having business program interfaces associated with a first software microservice presence may be determined by: the method comprises the steps of determining first micro-service associated information based on micro-service architecture information of the software service, wherein the first micro-service associated information comprises associated service program interface information among micro-service programs of a plurality of service operation nodes in the software service, the number of the associated service program interface information is at least one, one associated service program interface information comprises a group of service operation node labels, and the group of service operation node labels comprises at least two service operation node labels. If the at least one group of service operation node labels comprises service operation node labels of service operation nodes in the first software microservice, and service operation node labels of service operation nodes in the software microservice which are associated with the logic service exist in the group of service operation node labels of service operation nodes in the first software microservice, determining that other software microservices which are associated with the first software microservice exist, wherein the software microservice which is associated with the logic service exists is the software microservice which is adjacent to the first software microservice in the plurality of software microservices.

That is, the associated service program interface information between the micro service programs of the plurality of service operation nodes in the software service may be determined according to the micro service architecture information of the software service, and since one associated service program interface information includes a set of service operation node tags, at least one set of service operation node tags may be determined, if the determined at least one set of service operation node tags includes the service operation node tag of the service operation node in the first software micro service, and a set including the service operation node tag of the service operation node in the first software micro service includes a service operation node tag of a service operation node in the software micro service having a logical service association, that is, a reference set including the service operation node tag of the service operation node in the first software micro service and the service operation node in the software micro service having a logical service association exists in the determined at least one set of service operation node tags And the service operation node label of the service operation node indicates that the software micro-service associated with the logic service exists an associated service program interface with the first software micro-service, so that other software micro-services with the associated service program interface with the first software micro-service can be determined to exist.

Further, in one possible design example, if there are other software microservices having associated business program interfaces with the first software microservice, the first unit service representation corresponding to the first software microservice may be stored in the representation cache sequence, so that the first unit service representation may be directly obtained from the cache when the subsequent service representations are associated. If no other software microservice with the associated service program interface exists in the first software microservice, the first unit service portrait corresponding to the first software microservice does not need to be associated with the whole service portrait, and the step of determining the first unit service portrait corresponding to the first software microservice can be continuously executed when the next portrait acquisition node starts.

In one possible design example, the digital content server 100 may store in advance a correspondence relationship between the software microservice and the plurality of service execution nodes, and a layout relationship between the plurality of software microservices. Thus, after determining at least one set of service running node tags, the software microservice to which the service running node indicated by each service running node tag belongs can be determined, and therefore, the service operation node label belonging to the first software microservice and the service operation node label belonging to the software microservice with logic business association in at least one group of service operation node labels can be determined according to the corresponding relation between the service operation node labels and the software microservices, and further in case it is determined that there is a reference group in at least one group of service running node tags, and that the reference group comprises the service running node tag of the service running node within the first software microservice and the service running node tag of the service running node within the software microservice associated with the logical service, other software microservices that have associated business program interfaces with the first software microservice may be considered to exist within the software business service.

In one possible design example, it may be determined that the past overall service representation includes service representations corresponding to other software microservices for which the first software microservice has an associated business program interface by: when determining that there are other software microservices having associated service program interfaces with the first software microservice in the software service, acquiring all groups including the service running node labels of the service running nodes in the first software microservice and the service running node labels of the service running nodes in the software microservices having logical service associations, determining the software microservice identifier of each software microservice having logical service associations, and obtaining the overlapped software microservice identifier. Each whole service portrait in the past whole service portraits can comprise the software micro-service identification of the corresponding software micro-service, so that whether the service portraits corresponding to the associated service program interface identification exist in the past whole service portraits or not can be determined, and if the service portraits exist, the past whole service portraits can be considered to comprise the service portraits corresponding to other software micro-services of which the first software micro-service has the associated service program interface.

In one possible design example, if the service representation corresponding to another software microservice having an associated business program interface with the first software microservice is not included in the past whole service representation, the first unit service representation corresponding to the first software microservice may be stored in the database.

In one possible design example, for ease of description, a service representation of a past overall service representation corresponding to other software microservices for which the first software microservice has an associated business program interface is referred to as an other service representation. Specific implementation of determining that a first unit service representation corresponding to a first software microservice in a past whole service representation belongs to a service representation of the same service object may include: the method comprises the steps of obtaining other service pictures, determining the matching degree of each first unit service picture and other service pictures for each first unit service picture corresponding to a first software microservice, if the other service pictures with the matching degree larger than a matching degree threshold value exist for each first unit service picture, determining that the whole service pictures in the past comprise service pictures which belong to the same service object as each first unit service picture, determining the service pictures with the matching degree larger than the matching degree threshold value as service pictures which belong to the same service object as each corresponding first unit service picture, and associating the service pictures which belong to the same service object with the corresponding first unit service pictures.

In one possible design example, for a reference first unit service representation in the first software microservice, the reference first unit service representation may be compared with each other service representation to determine a matching degree of the reference first unit service representation with each other service representation, if there is another service representation whose matching degree is greater than a threshold matching degree, the other service representation whose matching degree is greater than the threshold matching degree is determined as a service representation belonging to the same service object as the reference first unit service representation, and the overall service tag of the reference first unit service representation is determined as the overall service tag of the service representation belonging to the same service object.

Wherein, the reference first unit service image is one unit service image in a plurality of first unit service images in the first software micro service.

Wherein the integral service tag is used for uniquely identifying a service object within the software business service.

In one possible design example, assume that the first software microservice is software microservice 2, other software microservices having associated business program interfaces with software microservice 2 include software microservice 1 and software microservice 3, and the overall service representation in the past includes service representations corresponding to software microservice 1 and software microservice 3. It is assumed that the software microservice 1 corresponds to a service image corresponding to the entire service tag a and a service image corresponding to the entire service tag B in the past entire service image, the software microservice 2 corresponds to a service image corresponding to the entire service tag B and a service image corresponding to the entire service tag C in the past entire service image, and the software microservice 3 corresponds to a service image corresponding to the entire service tag C. The first unit service images corresponding to the software microservice 2 are referred to as service image D, service image E and service image F.

For service portrait D, the matching degree of service portrait D and service portrait corresponding to integral service label A can be respectively determined, the matching degree of service portrait D and service portrait corresponding to integral service label B can be determined, the matching degree of service portrait D and service portrait corresponding to integral service label C can be determined, if the matching degree of service portrait D and service portrait corresponding to integral service label A is greater than the threshold value of the matching degree, the service portrait D and service portrait corresponding to integral service label A can be determined to be the same service object, the integral service label corresponding to service portrait D can be determined to be A, the service portrait corresponding to the integral service label A at the previous portrait acquisition node is associated with the service portrait determined by the current portrait acquisition node, and further the service portrait of the service object indicated by integral service label A at different portrait acquisition nodes in different software micro services can be associated, the service object partition image correlation indicated by the whole service label A is realized.

Similarly, for service image E, the matching degree of service image corresponding to service image E and whole service label A, the matching degree of service image corresponding to service image E and whole service label B, the matching degree of service image corresponding to service image E and whole service label C can be respectively determined, assuming that the matching degree of service image corresponding to service image E and whole service label B is greater than the matching degree threshold value, the service image corresponding to service image E and whole service label B can be determined to be the same service object, the whole service label corresponding to service image E can be determined to be B, the service image corresponding to last image acquisition node of whole service label B is associated with the service image determined by current image acquisition node, and further the service image of service object indicated by whole service label B in different image acquisition nodes in different software microservices is associated, the service object partition image correlation indicated by the whole service label B is realized.

Similarly, for service image F, the matching degree of service image corresponding to service image F and entire service label A, the matching degree of service image corresponding to service image F and entire service label B, the matching degree of service image corresponding to service image F and entire service label C, and the matching degree of service image corresponding to service image E and entire service label B is assumed to be greater than the threshold value of matching degree, it can be determined that the service representation F is the same service object as the service representation corresponding to the entire service tag C, the overall service tag corresponding to the service image F can be determined as C, the service image corresponding to the previous image obtaining node of the overall service tag C can be associated with the service image determined by the current image obtaining node, further, the service objects indicated by the whole service label C are associated with the service images in different image acquisition nodes in the software microservice 2.

Further, after determining the matching degree of the reference first unit service image with each other service image, if there is no other service image having a matching degree with the reference unit service image greater than the threshold matching degree, a new overall service tag may be determined for the reference first unit service image.

In a possible design example, after the first unit service portrait corresponding to each software microservice in the software business service is associated, an overall service label of each first unit service portrait in the software business service can be determined, then, the service portraits identical to the overall service labels in the plurality of software microservices are collected into one service portrait, that is, each overall service portrait in the finally obtained overall service portrait of the service object in the software business service corresponds to one overall service label, that is, each overall service portrait corresponds to one service object.

Further, after the overall service portrait of the service object in the software service is determined, the overall service portrait determined in the current portrait acquisition node can be stored in the database.

Further, on the basis of the embodiment of the present application, the above method may further include the following steps:

step S1101 is to obtain service operation range information of a plurality of service operation nodes in the software service, service operation range information of the node, and service configuration information of the software service, where the service configuration information is information configured in association with the service operation nodes and service objects in the software service.

In one possible design example, a software microservice may be represented by Micro (M1, M2, M3.). When i takes different values, Micro represents different software Micro services, M1 represents a service operation node 1 in the software Micro services, and so on, and Mi represents a service operation node in the software Micro services.

In one possible design example, the service operation range information of the service operation node includes service operation service area information of the corresponding service operation node, the service operation range information of the node includes service entity attribute boundary area information of the node, and the service configuration information includes distribution of the service operation node in the software service, service data amount distribution of the service object, and comprehensive image classification demand of the service object. The service operation service area information comprises the number of service items of corresponding service operation nodes, the service entity attribute boundary area information refers to the number of service data fragments of the most entity attribute edge of a single image classification network model which is included by the node and can carry out correlation calculation, the distribution condition refers to the distribution average number of the service operation nodes corresponding to the associated service program interface in the software service, the service data quantity distribution refers to the data quantity of service objects in a unit statistical time period unit statistical area in the software service, and the comprehensive image classification demand refers to the average number of times of image classification of the service objects in unit time in the software service.

In a possible design example, the service operation range information of the service operation node is related to a preset configuration of the service operation node itself, and the number of the service items of each service operation node may be determined as the service operation range information of each corresponding service operation node.

In a possible design example, the service operation range information of the node is related to a preset configuration of the digital content server 100 itself, and the number of service data fragments of the most entity attribute edge (i.e., the service entity attribute boundary area information of the node) that are subjected to the correlation calculation by the single image classification network model of the node may be determined as the service operation range information of the node.

In one possible design example, determining a distribution of service operation nodes within a software business service may include: determining third micro-service business service associated information according to micro-service architecture information of the software business service, wherein the third micro-service business service associated information comprises the number of associated business program interfaces in the software business service and the number of service operation nodes corresponding to each associated business program interface, determining the total number of the service operation nodes corresponding to a plurality of associated business program interfaces in the software business service based on the number of the service operation nodes corresponding to each associated business program interface, and determining the quotient of the total number and the number of the associated business program interfaces as the distribution condition of the service operation nodes. Therefore, the distribution situation of the service operation nodes in the software business service is related to the layout of the service operation nodes in the software business service, and if the layout of the service operation nodes in the software business service is not changed, the distribution situation is not changed.

In one possible design example, the service data volume distribution of the service objects may be an empirical value obtained by the user from big data, or may be determined according to the total number of service objects in the software service in a certain time period, the portrait classification time of the time period, and the area of the software service.

In one possible design example, the comprehensive portrait classification requirement of the service object may be an empirical value obtained by a user according to big data, or may be determined according to the total live portrait classification time of the service object in a certain time period of the software business service and the portrait classification time of the time period.

Step S1102, determining a number of portrait classification network models based on the service operation range information of the plurality of service operation nodes, the service operation range information of the node, and the service configuration information, the number of portrait classification network models being a number of portrait classification network models required to generate an overall service portrait of a service object within the software business service.

That is, when the software business service is divided, the number of the portrait classification network models needs to be determined according to the service operation range information of the service operation node, the service operation range information of the node, and the service configuration information configured in association with the service operation node and the service object in the software business service, so as to improve the calculation efficiency of determining the first unit service portrait without wasting the resources of the digital content server 100.

In one possible design example, the specific implementation of determining the number of portrait classification network models based on the service operation range information of the plurality of service operation nodes, the service operation range information of the node, and the service configuration information may include: and determining the number of the portrait classification network models based on service operation service area information of a plurality of service operation nodes, service entity attribute boundary area information of the node, the distribution condition of the service operation nodes in the software service, the service data volume distribution of service objects and the comprehensive portrait classification demand of the service objects.

In one possible design example, the number of the portrait classification network models may be determined based on service operation service area information of a plurality of service operation nodes, service entity attribute boundary area information of the node, distribution of service operation nodes in the software service, service data amount distribution of service objects, and comprehensive portrait classification demand of the service objects, by the following formula:

R=（R1*R2*R3*T）/R4

wherein, R represents the number of portrait classification network models, R1 represents the average service operation service area information of a plurality of service operation nodes, R2 represents the distribution situation of the service operation nodes, R3 represents the service data volume distribution of service objects, T represents the comprehensive portrait classification demand of the service objects, and R4 represents the service entity attribute boundary area information of the node.

Among them, the distribution average number of service execution service area information of the plurality of service execution nodes (i.e., the distribution average number of the service item numbers of the plurality of service execution nodes) may be determined as the average service execution service area information R1 of the plurality of service execution nodes.

Step S1103, based on the number of the portrait classification network models and the micro-service architecture information of the software service, the software service is divided into a plurality of software micro-services.

The micro service architecture information may be determined in advance based on a service operation node layout, a service function layout, and the like in the software business service.

In one possible design example, the specific implementation of dividing the software business service into a plurality of software microservices based on the number of portrait classification network models and microservice architecture information of the software business service may include:

(1) and dividing the number of service operation nodes included in the software service by the number of the portrait classification network models to obtain a target value, and determining second micro-service associated information based on the micro-service architecture information.

In this embodiment, the second micro-service association information includes micro-service location information of a micro-service program in which each service operation node is located and location information of a micro-service segmentation node in the software service.

(2) And traversing a plurality of service operation nodes in the software business service.

(3) And when traversing to one service operation node, if the micro-service positioning information of the micro-service program where the currently traversed service operation node is located, the micro-service positioning information of the micro-service program where the last traversed service operation node is located, and the positioning information of the micro-service segmentation node in the software service are based.

(4) And determining that the currently traversed service operation node and the last traversed service operation node are in the same linkage micro-service program, and determining the number of service operation nodes in the software micro-service corresponding to the last traversed service operation node.

(5) And if the number of service operation nodes in the software micro service corresponding to the last traversed service operation node is less than the target value, dividing the micro service program corresponding to the currently traversed service operation node into the software micro service corresponding to the last traversed service operation node.

Wherein the target value may be used to describe the number of service operation nodes that may be partitioned within each software microservice.

Wherein the micro-service location information may be used to indicate an accessible data area in the software business service.

That is to say, a service object value of a service operation node which each software micro service can include can be determined according to the number of network elements and the number of service operation nodes in the software service, then, based on pre-generated micro service architecture information, micro service positioning information of an area where each service operation node is located and positioning information of a micro service segmentation node in the software service are determined, a plurality of service operation nodes in the software service are traversed, and each time one service operation node is traversed, whether the currently traversed service operation node and the last traversed service operation node are located in the same linked micro service program can be determined, if yes, whether the number of service operation nodes in the software micro service corresponding to the last traversed service operation node is smaller than a target value is continuously determined, and if yes, the currently traversed service operation node is divided into the software micro service corresponding to the last traversed service operation node In (1).

In a possible design example, a specific implementation manner for determining that the currently traversed service operation node and the last traversed service operation node are in the same connected domain based on the micro-service location information of the micro-service program in which the currently traversed service operation node is located, the micro-service location information of the micro-service program in which the last traversed service operation node is located, and the location information of the micro-service split node in the software service may include: if the micro-service split node between the micro-service program where the currently traversed service operation node is located and the micro-service program where the last traversed service operation node is located does not exist in the software service based on the positioning information of the micro-service split node in the software service, it can be determined that the currently traversed service operation node and the last traversed service operation node are in the same linked micro-service program.

Further, after traversing to a service operation node, if the current traversed service operation node is in a different linkage micro service program from the last traversed service operation node based on the micro service positioning information of the micro service program in which the current traversed service operation node is located, the micro service positioning information of the micro service program in which the last traversed service operation node is located, and the positioning information of the micro service split node in the software service, the micro service program corresponding to the current traversed service operation node is determined as a new software micro service.

That is, if it is determined that the currently traversed service operation node and the last traversed service operation node are in different linked micro-service programs, the micro-service program of the currently traversed service operation node and the micro-service program of the last traversed service operation node cannot be divided into the same software micro-service, and the micro-service program of the currently traversed service operation node can be determined as a new software micro-service.

Further, after determining the number of service operation nodes in the software microservice corresponding to the last traversed service operation node, the method may further include: and if the number of the service operation nodes in the software micro service corresponding to the last traversed service operation node is greater than or equal to the target value, determining the micro service program corresponding to the currently traversed service operation node as a new software micro service.

That is, if the number of service operation nodes in the software microservice corresponding to the previous service operation node is greater than or equal to the target value, and then the current service operation node is determined to the software microservice corresponding to the previous service operation node, the efficiency of determining the first unit service representation corresponding to the software microservice may be reduced, and therefore, the microservice program corresponding to the service operation node traversed currently may be determined to be a new software microservice.

Further, if the currently traversed service operation node is the first traversed service operation node, that is, there is no last traversed service operation node, the micro service program of the currently traversed service operation node may be determined as a new software micro service.

In addition, after the software micro-service to which the micro-service program of one service operation node traversed belongs is determined, whether the service operation node in the software service is traversed or not can be judged, if yes, traversal is stopped, a plurality of software micro-services are obtained, and if not, traversal is continued for the next service operation node.

In a possible design example, on the basis of the above description, the above method may further include the following steps, which are described in detail below.

Step S113, sending push hotspot information to the mobile software server 200 corresponding to the service object according to the overall service representation of the service object in the software service.

Among them, the above step S113 may be implemented by the following exemplary substeps.

And a substep S1131, acquiring target hotspot subject distribution information containing the candidate hotspot data source according to the whole service portrait, and performing subject migration behavior recognition processing on the target hotspot subject distribution information to obtain hotspot subject migration behavior content corresponding to the target hotspot subject distribution information.

And a sub-step S1132, acquiring a target topic extraction model corresponding to the target hotspot topic distribution information, extracting a first topic migration component and a second topic migration component from the hotspot topic migration behavior content through the target topic extraction model, and performing topic migration component fusion on the first topic migration component and the second topic migration component to obtain a global topic migration component of the topic migration behavior content associated with the target hotspot topic distribution information.

And the substep S1133, according to the global theme migration component of the theme migration behavior content and the target theme extraction model, performing theme complete migration event analysis on the hot theme migration behavior content to obtain a theme complete migration event analysis result corresponding to the hot theme migration behavior content.

In the substep S1134, if the analysis result of the topic complete migration event indicates that the hotspot topic migration behavior content meeting the topic complete migration indexes exists in the target hotspot topic distribution information, determining the candidate hotspot data source as the target data source, and confirming a data pushing process for instructing to push the target hotspot topic distribution information to the mobile software server 200.

Illustratively, in one possible design example, the number of content blocks of the above hot topic migration behavior content is multiple. For sub-step S1132, it can be realized by the following embodiments, which are described in detail as follows.

And a substep S11321, obtaining a target topic extraction model corresponding to the target hotspot topic distribution information. The target topic extraction model comprises: a subscription topic migration component extraction network and a non-subscription topic migration component extraction network.

And a substep S11322, extracting a topic migration component code segment from each hot topic migration behavior content through the subscription topic migration component extraction network, and determining a first topic migration component according to the extracted topic migration component code segment of each hot topic migration behavior content.

And the substep S11323, extracting topic migration component change information from each hot topic migration behavior content through the non-subscription topic migration component extraction network, and determining a second topic migration component according to the extracted topic migration component change information of each hot topic migration behavior content.

And in the substep S11324, performing topic migration component fusion on the first topic migration component of each hotspot topic migration behavior content and the second topic migration component of the corresponding hotspot topic migration behavior content to obtain a global topic migration component of each hotspot topic migration behavior content, and determining the global topic migration component of each hotspot topic migration behavior content as the global topic migration component of the topic migration behavior content associated with the target hotspot topic distribution information.

It is worth to be noted that the target topic extraction model may include: the migration identifies the network. The migration identification network is used for carrying out migration node tracking on the hot topic distribution information to which the hot topic migration behavior content in the target hot topic distribution information belongs.

On this basis, the substep S1133 can be implemented by the following embodiments, which are described in detail below.

In the substep S11331, the global topic migration component of the topic migration behavior content is input to the migration recognition network in the target topic extraction model, and the migration recognition network determines migration matching information between the global topic migration component of the topic migration behavior content and the plurality of sample topic migration components in the migration recognition network. The migration matching information is used for representing migration relationship information of the global theme migration component of the theme migration behavior content and the same hotspot theme distribution information corresponding to each sample theme migration component.

In the sub-step S11332, based on the migration matching information, a sample topic migration component having a maximum migration component value with the global topic migration component of the topic migration behavior content is obtained from the plurality of sample topic migration components, and the sample topic migration component having the maximum migration component is used as the target sample topic migration component.

In the substep S11333, the sample topic distribution corresponding to the target sample topic migration component is used as target hotspot topic distribution information corresponding to the global topic migration component of the topic migration behavior content, and based on the target hotspot topic distribution information and the maximum migration component associated with the target hotspot topic distribution information, a topic complete migration event analysis result after migration node tracking is performed on the hotspot topic migration behavior content in the target hotspot topic distribution information is determined.

Fig. 3 is a schematic diagram of functional modules of an information cloud computing pushing apparatus 300 combined with a big data portrait according to an embodiment of the present disclosure, and this embodiment may divide the functional modules of the information cloud computing pushing apparatus 300 combined with a big data portrait according to the method embodiment executed by the digital content server 100, that is, the following functional modules corresponding to the information cloud computing pushing apparatus 300 combined with a big data portrait may be used to execute the method embodiments executed by the digital content server 100. The information cloud computing pushing device 300 combined with a big data portrait may include a sending module 310, an obtaining module 320, a constructing module 330, and a pushing module 340, and the functions of the functional modules of the information cloud computing pushing device 300 combined with a big data portrait are described in detail below.

The sending module 310 is configured to send push hotspot information to the mobile software server 200 corresponding to the service object according to the overall service representation of the service object in the software service. The sending module 310 may be configured to execute the step S110, and the detailed implementation of the sending module 310 may refer to the detailed description of the step S110.

The obtaining module 320 is configured to obtain the push hotspot feedback behavior information fed back by the mobile software server 200 according to the push hotspot information, and obtain a first service feedback knowledge graph from the push hotspot feedback behavior information, where one entity in the first service feedback knowledge graph corresponds to one first service object, and an entity attribute between entities in the first service feedback knowledge graph is used to represent a service feedback interaction behavior between the first service objects. The obtaining module 320 may be configured to perform the step S120, and the detailed implementation of the obtaining module 320 may refer to the detailed description of the step S120.

A constructing module 330, configured to construct a second service feedback knowledge graph according to service update information of a newly added second service object and the first service feedback knowledge graph, where at least two entities in the second service feedback knowledge graph are respectively used to represent the second service object and at least one first service object having a service feedback interaction behavior with the second service object, and at least one entity attribute between entities in the second service feedback knowledge graph is respectively used to represent a service feedback interaction behavior between the second service object and the at least one first service object. The building block 330 may be configured to perform the step S130, and the detailed implementation of the building block 330 may refer to the detailed description of the step S120.

And the pushing module 340 is configured to determine, according to the second service feedback knowledge graph, service-related information of the second service object, so as to perform information pushing on the newly added second service object based on the service-related information, where the service-related information is used to represent an image updating feature of the second service object and a feedback interaction behavior image feature between the second service object and at least one first service object. The pushing module 340 may be configured to perform the step S140, and the detailed implementation manner of the pushing module 340 may refer to the detailed description of the step S140.

It should be noted that the division of each module of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical business state object, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the sending module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the sending module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

Fig. 4 is a schematic diagram illustrating a hardware structure of a digital content server 100 for implementing the information cloud computing pushing method with a large data representation according to the embodiment of the present disclosure, and as shown in fig. 4, the digital content server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the sending module 310, the obtaining module 320, the constructing module 330, and the pushing module 340 included in the information cloud computing pushing apparatus 300 with a big data image shown in fig. 3), so that the processor 110 may execute the information cloud computing pushing method with a big data image according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control transceiving actions of the transceiver 140, so as to perform data transceiving with the aforementioned mobile software service end 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the digital content server 100, which implement the principle and the technical effect similarly, and the detailed description of the embodiment is omitted here.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended ISA (EISA) bus, among others. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, a readable storage medium is provided, where computer execution instructions are stored, and when a processor executes the computer execution instructions, the information cloud computing pushing method combining the big data portrait is implemented as above.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An information cloud computing pushing method combined with big data images is applied to a digital content server, the digital content server is in communication connection with a plurality of mobile software servers, the digital content server is realized according to a cloud computing platform, and the method comprises the following steps:

2. The information cloud computing pushing method combining the big data portrait, according to claim 1, wherein the step of constructing the second business feedback knowledge graph according to the business service update information of the newly added second business service object and the first business feedback knowledge graph includes:

3. The information cloud computing push method in combination with a big data portrait according to claim 2, wherein the target business feedback interaction behavior is a direct interaction behavior and an indirect interaction behavior;

4. The method for pushing information cloud computing by combining the big data portrait, according to claim 2, wherein the step of constructing a second business feedback knowledge graph according to the target business feedback interaction behavior comprises:

5. The method for pushing information through cloud computing in combination with big data portraits according to claim 2, wherein after the step of constructing a second business feedback knowledge graph according to the target business feedback interaction behavior, the method further comprises:

6. The method for pushing information cloud computing with a big data portrait according to claim 1, wherein the step of determining business service related information of the second business service object according to the second business feedback knowledge graph comprises:

7. The information cloud computing pushing method combined with the big data portrait according to claim 6, wherein the step of determining the business service related information of the second business service object according to the feedback interaction behavior portrait characteristics of the business feedback knowledge graph and the portrait update characteristics of the business feedback knowledge graph includes:

8. The information cloud computing pushing method combined with the big data portrait according to any one of claims 1 to 7, wherein the step of sending the pushed hotspot information to the mobile software server corresponding to the service object according to the whole service portrait of the service object in the software business service includes:

determining a first unit service portrait corresponding to each software microservice in a plurality of software microservices based on microservice operation data of a service object sent by service operation nodes in the plurality of software microservices through a plurality of portrait classification network models respectively, wherein one portrait classification network model is used for determining a corresponding first unit service portrait based on microservice operation data of the service object in one software microservice, and the plurality of software microservices are obtained by carrying out service differentiation on software service;

determining an overall service portrait of a service object in the software business service based on a first unit service portrait corresponding to each software microservice in the plurality of software microservices;

sending push hotspot information to a mobile software server corresponding to a service object according to the whole service portrait of the service object in the software service;

the step of sending the pushed hotspot information to the mobile software server corresponding to the service object according to the whole service portrait of the service object in the software service comprises the following steps:

acquiring target hotspot subject distribution information containing candidate hotspot data sources according to the whole service portrait, and performing subject migration behavior identification processing on the target hotspot subject distribution information to obtain hotspot subject migration behavior content corresponding to the target hotspot subject distribution information;

acquiring a target theme extraction model corresponding to the target hotspot theme distribution information, extracting a first theme migration component and a second theme migration component from the hotspot theme migration behavior content through the target theme extraction model, and performing theme migration component fusion on the first theme migration component and the second theme migration component to obtain a global theme migration component of the theme migration behavior content associated with the target hotspot theme distribution information;

according to the global theme migration component of the theme migration behavior content and the target theme extraction model, carrying out theme complete migration event analysis on the hot theme migration behavior content to obtain a theme complete migration event analysis result corresponding to the hot theme migration behavior content;

and if the topic complete migration event analysis result represents that the target hotspot topic distribution information contains hotspot topic migration behavior content meeting several topic complete migration indexes, determining the candidate hotspot data source as a target data source, and confirming a data pushing process for indicating that the target hotspot topic distribution information is pushed to the mobile software server.

9. The information cloud computing push method in combination with a big data portrait according to claim 1, wherein the number of content blocks of the hot topic migration behavior content is plural; the step of obtaining a target topic extraction model corresponding to the target hotspot topic distribution information, extracting a first topic migration component and a second topic migration component from the hotspot topic migration behavior content through the target topic extraction model, and performing topic migration component fusion on the first topic migration component and the second topic migration component to obtain a global topic migration component of the topic migration behavior content associated with the target hotspot topic distribution information includes:

acquiring a target theme extraction model corresponding to the target hotspot theme distribution information; the target topic extraction model comprises: a subscription topic migration component extraction network and a non-subscription topic migration component extraction network;

extracting a topic migration component coding segment from each hot topic migration behavior content through the subscription topic migration component extraction network, and determining the first topic migration component according to the extracted topic migration component coding segment of each hot topic migration behavior content;

extracting theme migration component change information from each hot theme migration behavior content through the non-subscription theme migration component extraction network, and determining the second theme migration component according to the extracted theme migration component change information of each hot theme migration behavior content;

performing topic migration component fusion on a first topic migration component of each hot topic migration behavior content and a second topic migration component corresponding to the hot topic migration behavior content to obtain a global topic migration component of each hot topic migration behavior content, and determining the global topic migration component of each hot topic migration behavior content as the global topic migration component of the topic migration behavior content associated with the target hot topic distribution information;

wherein the target topic extraction model comprises: migrating the identification network; the migration identification network is used for carrying out migration node tracking on the hotspot topic distribution information to which the hotspot topic migration behavior content belongs in the target hotspot topic distribution information;

the obtaining a topic complete migration event analysis result corresponding to the hot topic migration behavior content by performing topic complete migration event analysis on the hot topic migration behavior content according to the global topic migration component of the topic migration behavior content and the target topic extraction model includes:

inputting the global theme migration component of the theme migration behavior content into the migration recognition network in the target theme extraction model, and determining migration matching information between the global theme migration component of the theme migration behavior content and a plurality of sample theme migration components in the migration recognition network by the migration recognition network; the migration matching information is used for representing migration relationship information of global theme migration components of the theme migration behavior content and the same hotspot theme distribution information corresponding to each sample theme migration component;

based on the migration matching information, acquiring a sample theme migration component with the maximum migration component value with the global theme migration component of the theme migration behavior content from the plurality of sample theme migration components, and taking the sample theme migration component with the maximum migration component as a target sample theme migration component;

and taking the sample theme distribution corresponding to the target sample theme migration component as target hotspot theme distribution information corresponding to the global theme migration component of the theme migration behavior content, and determining a theme complete migration event analysis result after migration node tracking is performed on the hotspot theme migration behavior content in the target hotspot theme distribution information based on the target hotspot theme distribution information and the maximum migration component associated with the target hotspot theme distribution information.

10. A digital content server, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one mobile software server, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the information cloud computing push method in combination with a big data portrait according to any one of claims 1 to 9.