CN112612979A

CN112612979A - Page service processing method based on cloud computing and artificial intelligence and block chain center

Info

Publication number: CN112612979A
Application number: CN202011500058.4A
Authority: CN
Inventors: 倪小红
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-17
Filing date: 2020-12-17
Publication date: 2021-04-06

Abstract

The embodiment of the application provides a page service processing method based on cloud computing and artificial intelligence and a block chain center, wherein a page form service is configured by configuring a page reference resource of a page service to be updated based on a current page push element, a candidate push data source object and a configured configuration to obtain an updated page form service, and the updated page source data service is determined by using the updated push form object and the current page push element, so that a more accurate updated page source data service can be obtained, then the updated page source data service and the updated page form service are used for configuring the page push service when a termination condition is met, and the problem of low image matching rate generated when the page service is pushed and configured is avoided because the updated page source data service and the updated page form service are used for configuring the page push service, the allocated page push service is more suitable for the portrait prediction result.

Description

Page service processing method based on cloud computing and artificial intelligence and block chain center

Technical Field

The application relates to the technical field of page service configuration, in particular to a page service processing method based on cloud computing and artificial intelligence and a block chain center.

Background

In the page operation process, the page operation behavior big data uploaded by the block chain financial display terminal usually contains rich information content, wherein some behavior process information may contain important information concerned by the user, if the behavior process information can be analyzed to generate a corresponding portrait result, the content of the page is updated in a targeted manner, and the user can obtain better content service experience.

The configuration scheme of the page push service relates to the information browsing experience of the page content displayed on the block chain financial display terminal. At present, most of push configuration of page services is usually updated only based on an index rule of page content, and does not consider page source data services (such as services providing a source data reading interface and a reading rule) and updated page table sheet services (such as services adjusting refresh rates and area sizes of different page table areas) in the process of updating the page services, so that the matching rate of the configured page push services and portrait prediction results is not high when the page services are pushed and configured, and the configured page push services do not accord with the portrait prediction results.

Disclosure of Invention

In order to overcome at least the above-mentioned deficiencies in the prior art, the present application aims to provide a page service processing method and a blockchain center based on cloud computing and artificial intelligence, wherein at each iteration, a push data source object is selected by using an update page form service, so that a more accurate push data source object can be selected, then an update page source data service corresponding to a current page push service is determined by using the update push form object and a current page push element, so that at each iteration, the update page source data service is determined by using the update push form object and the current page push element, so that the more accurate update page source data service can be obtained, then when a termination condition is met, the page push service configuration is performed by using the update page source data service and the update page table form service, and as the page push service configuration is performed by using the update page source data service and the page update table form service, the problem of low matching rate with the portrait prediction result generated when the page service is pushed and configured is avoided, so that the configured page pushing service is more in line with the portrait prediction result.

In a first aspect, the present application provides a page service processing method based on cloud computing and artificial intelligence, which is applied to a blockchain center, where the blockchain center is in communication connection with a plurality of blockchain financial display terminals, and the method includes:

obtaining a portrait prediction result of each analyzable behavior process information in page operation behavior big data uploaded by the block chain financial display terminal, and configuring a page reference resource of a page service to be updated according to the portrait prediction result of each analyzable behavior process information, wherein the page reference resource comprises a page typesetting source object and a page data source object, the portrait prediction result of each analyzable behavior process information is a portrait prediction result obtained based on at least one behavior portrait prediction service based on a cloud computing container, and the behavior portrait prediction service is obtained based on artificial intelligence model training;

selecting a current page push element from a current page push service corresponding to the page service to be updated, acquiring a corresponding candidate push data source object based on the page service to be updated, and performing page form service configuration based on the current page push element, the candidate push data source object and the page reference resource to obtain an updated page form service;

selecting an updated push form object from the current page push service according to an updated page form service, determining an updated page source data service corresponding to the current page push service according to the updated push form object and the current page push element, performing form binding on the updated push form object and the current page push element based on the updated page form service to obtain a page configuration resource, updating the current page push element and a candidate push data source object according to first difference information of the page configuration resource and the page reference resource, and returning to the page form service configuration until a first termination condition is met;

and performing page push service configuration based on the updated page source data service and the updated page form service which meet the first termination condition to obtain a target page push service corresponding to the page service to be updated.

In a possible implementation manner of the first aspect, the updating, according to the first difference information between the page configuration resource and the page reference resource, the current page push element and the candidate pushed data source object, and returning to the step of page form service configuration until a first termination condition is met includes:

determining to obtain first difference information based on the page configuration resource and the page reference resource, and updating the current page push service based on the updated page source data service to obtain an updated page push service when the first difference information does not meet a first termination condition;

and selecting an updated page push element from the updated page push service to obtain an updated current page push element, taking the updated push form object as an updated candidate push data source object, and returning to the step of performing page form service configuration based on the current page push element, the candidate push data source object and the page reference resource to obtain an updated page form service until a first termination condition is met.

In one possible implementation of the first aspect:

the page service to be updated is a page initial trigger service, and the page configuration resource comprises a page configuration typesetting source object and a page configuration data source object;

the determining to obtain first distinguishing information based on the page configuration resource and the page reference resource includes:

determining to obtain first distinguishing information based on the page configuration typesetting source object and the page typesetting source object, and determining to obtain second distinguishing information based on the page configuration data source object and the page data source object;

obtaining first distinguishing information of the page configuration resource and the page reference resource based on the second distinguishing information and the first distinguishing information; or

The page service to be updated is a non-page initial trigger service, and the page configuration resource comprises a page configuration typesetting source object and a page configuration data source object;

acquiring a forward page source data service corresponding to a forward trigger service of the non-page starting trigger service, wherein the forward page source data service is a page source data service used by the forward trigger service when a page push service is configured;

determining extension difference information of the forward page source data service and the updated page source data service, and obtaining first difference information of the page configuration resource and the page reference resource based on the second difference information, the first difference information and the extension difference information.

In a possible implementation manner of the first aspect, the service of the page to be updated is a page start trigger service;

the obtaining of the corresponding candidate pushed data source object based on the page service to be updated includes:

acquiring each preset trigger point, and selecting a current trigger point from each preset trigger point;

filling the current page pushing element into a page logic area according to the current trigger point to obtain a trigger point filling type-setting source object, and performing page form service configuration on the basis of the trigger point filling type-setting source object and the page type-setting source object to obtain a trigger point page form service;

selecting a trigger point push data source object from a page push service data source object area of the current page push service according to the trigger point page form service;

performing trigger point page form service configuration based on the trigger point pushing data source object, the current page pushing element and the page reference resource to obtain trigger point updating page form service;

selecting a trigger point from the page push service data source object area to update a push form object according to the trigger point update page form service;

determining a trigger point update page source data service corresponding to the current page push service according to the trigger point update push form object and the current page push element;

performing form binding on the trigger point update push form object and the current page push element based on the trigger point update page form service to obtain a trigger point page configuration resource, and updating the current page push service based on the trigger point update page source data service to obtain a trigger point update page push service when the second distinguishing information does not meet a second termination condition;

selecting a trigger point update page push element from the trigger point update page push service, using the trigger point update page push element as a current page push element, using the trigger point update push form object as a trigger point push data source object, and returning to perform trigger point page form service configuration based on the trigger point push data source object, the current page push element and the page reference resource to obtain a step of obtaining a trigger point update page form service until a second termination condition is met, so as to obtain current second difference information corresponding to the current trigger point;

traversing each preset trigger point to obtain each current second difference information corresponding to each preset trigger point, comparing each current second difference information to obtain target second difference information, using the preset trigger point corresponding to the target second difference information as a target trigger point, and filling the current page push element into a page logic area according to the target trigger point to obtain a filling typesetting source object;

acquiring a first initial page form service corresponding to the page initial trigger service, and filling the current page push element into a page logic area based on the first initial page form service to obtain a first initial filling typesetting source object;

determining to obtain third difference information based on the first initial filling and typesetting source object and the page typesetting source object;

adjusting the first initial page form service according to the third difference information, and returning to the step of filling the current page push element into a page logic area based on the first initial page form service to obtain a first starting filling typesetting source object until the third difference information meets a third termination condition;

taking the first initial page form service meeting the third termination condition as an initial page form service;

and selecting a candidate pushed data source object corresponding to the page starting trigger service from a pushed data source object set of the current page pushing service according to the starting page form service.

In a possible implementation manner of the first aspect, the service of the page to be updated is a non-page start trigger service;

acquiring a forward push data source object corresponding to a forward trigger service of the non-page starting trigger service, wherein the forward push data source object is a push data source object in a page push service corresponding to the forward trigger service;

and taking the forward push data source object as the candidate push data source object.

the performing page form service configuration based on the current page pushing element, the candidate pushing data source object and the page reference resource to obtain an updated page form service, including:

acquiring a second initial page form service corresponding to the page starting trigger service, and filling the current page push element and the candidate push data source object into a page logic area based on the second initial page form service to obtain a starting page configuration resource;

determining to obtain fourth distinguishing information based on the initial page configuration resource and the page reference resource;

adjusting form parameters of each form area in the second initial page form service according to the fourth difference information, and returning to the step of filling the current page push element and the candidate push data source object into a page logic area based on the second initial page form service to obtain a starting page configuration resource until the fourth difference information meets a fourth termination condition, wherein the form parameters comprise content refresh rate and area size of the form area;

and taking the second initial page form service meeting the fourth termination condition as an updated page form service corresponding to the page starting trigger service.

acquiring a third initial page form service corresponding to the non-page starting trigger service, and filling the current page push element and the candidate push data source object into a page logic area according to the third initial page form service to obtain a non-starting page configuration resource;

determining to obtain fifth difference information based on the non-initial page configuration resource and the page reference resource, and obtaining a forward page form service corresponding to a forward trigger service of the non-page initial trigger service, wherein the forward page form service is a page form service of a page push service corresponding to the forward trigger service;

determining posture distinguishing information of the forward page form service and the third initial page form service, and obtaining target fifth distinguishing information according to the fifth distinguishing information and the posture distinguishing information;

adjusting a third initial page form service corresponding to the non-page starting trigger service according to the target fifth differential information, and returning to the step of filling the current page push element and the candidate push data source object into a page logic area according to the third initial page form service to obtain a non-starting page configuration resource until the target fifth differential information meets a fifth termination condition;

and taking the third initial page table service meeting the fifth termination condition as an updated page table service corresponding to the non-page starting trigger service.

In a possible implementation manner of the first aspect, the selecting, according to the updated page table form service, an updated push form object from the current page push service includes:

acquiring a preset number of data source elements in a page push service data source object area of the current page push service, determining the current data source elements from the preset number of data source elements, selecting an initial push page template tag from the current data source elements, and determining tag description information of the initial push page template tag;

acquiring template label description information, and performing relational operation according to the label description information and the template label description information to obtain relational information;

when the relationship information does not meet the preset relationship condition, returning to the step of selecting the initial pushed page template tag from the current data source element, and when the relationship information meets the preset relationship condition, taking the initial pushed page template tag meeting the preset relationship condition as a pushed page expansion object corresponding to the current data source element;

filling each push page extension object into a page logic area according to the updated page form service to obtain each extension subscription filling object;

and determining to obtain sixth distinguishing information based on each extended subscription filling object and the page data source object, comparing the sixth distinguishing information corresponding to each extended subscription filling object to obtain target sixth distinguishing information, and taking a pushed page extension object corresponding to the target sixth distinguishing information as an updated pushed form object corresponding to the page data source object.

In a possible implementation manner of the first aspect, the step of obtaining an image prediction result of each analyzable behavior process information in the large page operation behavior data uploaded by the blockchain financial presentation terminal includes:

when detecting page operation behavior big data uploaded by the block chain financial display terminal, extracting at least one analyzable behavior process information from the page operation behavior big data; the at least one analyzable behavior process information is key behavior process information in the page operation behavior big data;

acquiring a preset behavior portrait prediction service set, and determining a plurality of tracking analysis associated parameters corresponding to the preset behavior portrait prediction service set aiming at each analyzable behavior process information in the at least one analyzable behavior process information; wherein each of the plurality of tracking analysis associated parameters characterizes an analysis association degree of each behavior portrait prediction service in the preset behavior portrait prediction service set to each of the at least one analyzable behavior process information; the preset behavior portrait prediction service set comprises at least one behavior portrait prediction service based on a cloud computing container;

determining a target behavior sketch prediction unit corresponding to each analyzable behavior process information from the preset behavior sketch prediction service set based on the plurality of tracking analysis associated parameters;

and performing portrait prediction on each analyzable behavior process information by using the target behavior portrait prediction unit corresponding to each analyzable behavior process information to obtain a portrait prediction result of each analyzable behavior process information.

In a second aspect, an embodiment of the present application further provides a page service processing apparatus based on cloud computing and artificial intelligence, which is applied to a blockchain center, where the blockchain center is in communication connection with a plurality of blockchain financial display terminals, and the apparatus includes:

the system comprises a first configuration module, a second configuration module and a third configuration module, wherein the first configuration module is used for acquiring a portrait prediction result of each analyzable behavior process information in page operation behavior big data uploaded by the block chain financial display terminal, and configuring page reference resources of a page service to be updated according to the portrait prediction result of each analyzable behavior process information, the page reference resources comprise a page typesetting source object and a page data source object, the portrait prediction result of each analyzable behavior process information is a portrait prediction result obtained based on at least one behavior portrait prediction service based on a cloud computing container, and the behavior portrait prediction service is obtained based on artificial intelligence model training;

the second configuration module is used for selecting a current page push element from a current page push service corresponding to the page service to be updated, acquiring a corresponding candidate push data source object based on the page service to be updated, and performing page form service configuration based on the current page push element, the candidate push data source object and the page reference resource to obtain an updated page form service;

an updating module, configured to select an updated push form object from the current page push service according to an updated page form service, determine an updated page source data service corresponding to the current page push service according to the updated push form object and the current page push element, perform form binding on the updated push form object and the current page push element based on the updated page form service to obtain a page configuration resource, update the current page push element and a candidate push data source object according to first difference information between the page configuration resource and the page reference resource, and return to the page form service configuration until a first termination condition is met;

and the third configuration module is used for carrying out page push service configuration on the basis of the updated page source data service and the updated page form service which meet the first termination condition to obtain a target page push service corresponding to the page service to be updated.

In a third aspect, an embodiment of the present application further provides a page service processing system based on cloud computing and artificial intelligence, where the page service processing system based on cloud computing and artificial intelligence includes a blockchain center and a plurality of blockchain financial display terminals communicatively connected to the blockchain center;

the blockchain center is configured to:

In a fourth aspect, an embodiment of the present application further provides a blockchain center, where the blockchain center includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one blockchain financial presentation terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the cloud computing and artificial intelligence based page service processing method in the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a determining machine-readable storage medium, where instructions are stored in the determining machine-readable storage medium, and when the instructions are executed, the determining machine is caused to perform the cloud computing and artificial intelligence based page service processing method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, the method includes configuring page reference resources of the page service to be updated by acquiring the page service to be updated, where the page reference resources include a page composition source object and a page data source object; selecting a current page push element from a current page push service corresponding to a page service to be updated, then carrying out page form service configuration according to the current page push element, a candidate push data source object and a page reference resource to obtain an updated page form service, selecting an updated push form object from the current page push service by using the updated page form service, selecting the push data source object by using the updated page form service at each iteration so as to select a more accurate push data source object, then determining an updated page source data service corresponding to the current page push service by using the updated push form object and the current page push element so as to determine the updated page source data service by using the updated push form object and the current page push element at each iteration so as to obtain the more accurate updated page source data service, and then when the termination condition is met, using the updated page source data service and the updated page table sheet service to configure the page pushing service, wherein the problem of low matching rate with the portrait prediction result generated when the page service is pushed and configured is avoided because the updated page source data service and the updated page table sheet service are used to configure the page pushing service, so that the configured page pushing service is more consistent with the portrait prediction result.

Drawings

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

Fig. 1 is a schematic view of an application scenario of a page service processing system based on cloud computing and artificial intelligence provided in an embodiment of the present application;

fig. 2 is a schematic flowchart of a page service processing method based on cloud computing and artificial intelligence according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of a page service processing apparatus based on cloud computing and artificial intelligence according to an embodiment of the present application;

fig. 4 is a schematic block diagram of a structural component of a blockchain center for implementing the cloud computing and artificial intelligence based page service processing method according to the embodiment of the present application.

Detailed Description

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

Fig. 1 is an interaction diagram of a cloud computing and artificial intelligence based page service processing system 10 according to an embodiment of the present application. The cloud computing and artificial intelligence based page service processing system 10 may include a blockchain center 100 and a blockchain financial presentation terminal 200 communicatively connected to the blockchain center 100. The cloud computing and artificial intelligence based page service processing system 10 shown in FIG. 1 is only one possible example, and in other possible embodiments, the cloud computing and artificial intelligence based page service processing system 10 may also include only some of the components shown in FIG. 1 or may also include other components.

Based on the inventive concept of the technical solution provided by the present application, the blockchain center 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 care, smart city management, smart industrial internet, general service monitoring management, and the like, and may also be applied to, for example and without limitation, new energy vehicle system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud vehicle management platform, blockchain financial data service platform, and the like, but is not limited thereto.

In this embodiment, the blockchain center 100 and the blockchain financial exhibition terminal 200 in the cloud computing and artificial intelligence based page service processing system 10 may cooperatively perform the cloud computing and artificial intelligence based page service processing method described in the following method embodiment, and the detailed description of the method embodiment may be referred to in the specific steps of the blockchain center 100 and the blockchain financial exhibition terminal 200.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flowchart of a page service processing method based on cloud computing and artificial intelligence according to an embodiment of the present application, where the page service processing method based on cloud computing and artificial intelligence according to the present embodiment may be executed by the blockchain center 100 shown in fig. 1, and the page service processing method based on cloud computing and artificial intelligence is described in detail below.

In step S110, a portrait prediction result of each analyzable behavior process information in the page operation behavior big data uploaded by the blockchain financial display terminal 300 is obtained, and a page reference resource of the page service to be updated is configured according to the portrait prediction result of each analyzable behavior process information.

The page service to be updated refers to a page service which needs to be configured by a page push service, and the page reference resource may include a page composition source object and a page data source object. The page layout source object refers to index information of a layout source object of the portrait label in the portrait prediction result of each analyzable behavior process information, and is used for controlling an index rule of a layout source (for example, a data source of a layout strategy of each large layout area). The page data source object refers to index information of a data source object according to the portrait label in the portrait prediction result of each analyzable behavior process information, and is used for controlling an index rule of a data source (such as a data source of each large portal website).

Specifically, the page service to be updated may be an initial trigger service of the page service, or may be a non-initial trigger service of the target service video.

For example, in some possible implementations, the page reference resource of the page service to be updated is configured according to the image prediction result of each analyzable behavior process information, and specifically, the page layout source object and the page data source object (which may be matched based on a pre-correspondence relationship, or artificially matched, etc.) that match the image tags corresponding to the image prediction result of each analyzable behavior process information may be configured as the page reference resource of the page service to be updated.

Step S120, selecting a current page push element from a current page push service corresponding to the page service to be updated, acquiring a corresponding candidate push data source object based on the page service to be updated, and performing page form service configuration based on the current page push element, the candidate push data source object and a page reference resource to obtain an updated page form service.

The current page pushing service refers to a current page pushing service corresponding to the page service to be updated, which is obtained according to a currently configured page pushing service model. The page push service model refers to preset page push services. The current page pushing element refers to the minimum form content unit pushed by the current page.

The page form service configuration refers to the evaluation of page form service based on form binding. The page form service is used for representing services for adjusting refresh rates, area sizes and the like of different page form areas.

Step S130, selecting an update push form object from the current page push service according to the update page form service, determining an update page source data service corresponding to the current page push service according to the update push form object and the current page push element, performing form binding on the update push form object and the current page push element based on the update page form service to obtain a page configuration resource, updating the current page push element and the candidate push data source object according to first difference information of the page configuration resource and the page reference resource, and returning to the page form service configuration until a first termination condition is met.

The updating of the push form object refers to a push form object obtained by updating the current page push service according to the update page form service. The updated page source data service refers to a page source data service obtained based on a page push service extension model according to an updated pushed form object and a current page push element, where the page source data service is used to represent index state parameters of page source data, such as index state parameters of a service providing a source data reading interface and a reading rule, for example, the index state parameters corresponding to the page source data service may include data characteristics and logic characteristics. The data characteristics refer to characteristics for representing data expression relations of the page source data services, and the logic characteristics refer to characteristics for representing structural expression relations of the page source data services. For example, the data feature may be related to form association data between the update pushed form object and the current page push element, and the logical feature may be related to form association logic between the update pushed form object and the current page push element.

Specifically, the current page push service may be updated according to the updated page table data service, a push data source object is selected from the updated page push service as an updated push form object, and then an updated page source data service corresponding to the current page push service is determined according to the updated push form object and the current page push element based on the page push service extension model.

Specifically, the updated push form object and the current page push element may be updated using an updated page form service to obtain an updated push page template tag, the converted push page template tag may pass through a form binding point page logic region to obtain a page configuration resource, the page configuration resource may include each page configuration/layout source object and each page configuration data source object, position difference information between each page configuration/layout source object and each corresponding page layout source object in the page reference resource may be determined, position difference information between each page configuration data source object and each corresponding page data source object in the page reference resource may be determined, and a set of the position difference information may be determined to obtain first difference information.

Determining an updated page source data service corresponding to the current page pushing service according to the updated push form object and the current page pushing element, performing form binding on the updated push form object and the current page pushing element based on the updated page form service to obtain a page configuration resource, and updating the current page pushing element and a candidate push data source object according to first difference information of the page configuration resource and the page reference resource

Step S140, performing page push service configuration based on the updated page source data service and the updated page form service that satisfy the first termination condition, to obtain a target page push service corresponding to the page service to be updated.

The first termination condition refers to a condition for configuring the page push service, and includes that the first distinguishing information is smaller than a preset threshold, reaches a preset iteration number, or obtains an updated page source data service and an updated page table single service without obvious change. The fact that the updated page source data service and the updated page table. The target page pushing service is a page pushing service obtained by using an updated page source data service and an updated page form service which meet a first termination condition to perform page pushing service configuration.

Specifically, when the terminal judges whether a first termination condition is met, if the first termination condition is met, the terminal executes page push service configuration based on the updated page source data service and the updated page table sheet service meeting the first termination condition, and if the first termination condition is not met, the terminal performs page table service configuration again based on the current page push element, the candidate pushed data source object and the page reference resource to obtain the updated page table sheet service. And continuously looping iteration until a first termination condition is met.

Based on the above steps, in the page push service configuration method, the page reference resources of the page service to be updated are configured by acquiring the page service to be updated, and the page reference resources include a page layout source object and a page data source object. Selecting a current page push element from a current page push service corresponding to a page service to be updated, then carrying out page form service configuration according to the current page push element, a candidate push data source object and a page reference resource to obtain an updated page form service, selecting an updated push form object from the current page push service by using the updated page form service, selecting the push data source object by using the updated page form service at each iteration so as to select a more accurate push data source object, then determining an updated page source data service corresponding to the current page push service by using the updated push form object and the current page push element so as to determine the updated page source data service by using the updated push form object and the current page push element at each iteration so as to obtain the more accurate updated page source data service, and then when the termination condition is met, using the updated page source data service and the updated page table sheet service to configure the page pushing service, wherein the problem of low matching rate with the portrait prediction result generated when the page service is pushed and configured is avoided because the updated page source data service and the updated page table sheet service are used to configure the page pushing service, so that the configured page pushing service is more consistent with the portrait prediction result.

In one possible implementation manner, for step S130, in the procedure of updating the current page push element and the candidate pushed data source object according to the first distinguishing information of the page configuration resource and the page reference resource, and returning to the page form service configuration until the first termination condition is met, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S131, determining to obtain first difference information based on the page configuration resource and the page reference resource, and updating the current page push service based on the updated page source data service to obtain an updated page push service when the first difference information does not meet a first termination condition.

And a substep S132 of selecting an updated page push element from the updated page push service to obtain an updated current page push element, taking the updated push form object as an updated candidate push data source object, and returning to the step of performing page form service configuration based on the current page push element, the candidate push data source object and the page reference resource to obtain an updated page form service until a first termination condition is met.

In this embodiment, the page service to be updated may be divided into a page start triggering service and a non-page start triggering service. The page start triggering service may be understood as a service (for example, some private services) that the page needs to be triggered by a certain pre-operation condition (for example, a login verification operation), and the non-page start triggering service may be understood as a service (for example, some public services) that the page does not need to be triggered by a certain pre-operation condition.

For example, in an alternative embodiment, the page to be updated service may be a page start trigger service, and the page configuration resource may include a page configuration typesetting source object and a page configuration data source object.

Thus, for sub-step S131, it can be realized by the following embodiments:

(1) and determining to obtain first distinguishing information based on the page configuration typesetting source object and the page typesetting source object, and determining to obtain second distinguishing information based on the page configuration data source object and the page data source object.

(2) And obtaining first distinguishing information of the page configuration resource and the page reference resource based on the second distinguishing information and the first distinguishing information.

Or in another alternative embodiment, the service for the page to be updated may be a non-page start trigger service, and the page configuration resource includes a page configuration typesetting source object and a page configuration data source object.

Thus, for sub-step S131, it can be realized by the following embodiments:

(2) And acquiring a forward page source data service corresponding to a forward trigger service of the non-page starting trigger service, wherein the forward page source data service is a page source data service used by the forward trigger service when the page push service is configured.

(3) And determining the extension difference information of the forward page source data service and the updated page source data service, and obtaining the first difference information of the page configuration resource and the page reference resource based on the second difference information, the first difference information and the extension difference information.

In a possible implementation manner, the service of the page to be updated is a page start triggering service. Thus, for step S120, in the process of acquiring the corresponding candidate pushed data source object based on the to-be-updated page service, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1201, acquiring each preset trigger point, and selecting a current trigger point from each preset trigger point.

And step S1202, filling the current page push element into a page logic area according to the current trigger point to obtain a trigger point filling and typesetting source object, and performing page form service configuration on the basis of the trigger point filling and typesetting source object and the page typesetting source object to obtain a trigger point page form service.

And a substep S1203, selecting a trigger point push data source object from a page push service data source object area of the current page push service according to the trigger point page form service.

And a substep S1204 of configuring a trigger point page form service based on the trigger point pushed data source object, the current page push element, and the page reference resource, to obtain a trigger point update page form service.

And a substep S1205, selecting a trigger point from the data source object area of the page push service according to the trigger point update page table form service to update the push table object.

And a substep S1206, determining a trigger point update page source data service corresponding to the current page push service according to the trigger point update push form object and the current page push element.

And a substep S1207, performing form binding on the trigger point update push form object and the current page push element based on the trigger point update page form service to obtain a trigger point page configuration resource, and updating the current page push service based on the trigger point update page source data service to obtain the trigger point update page push service when the second difference information does not meet the second termination condition.

And a substep S1208, selecting a trigger point update page push element from the trigger point update page push service, using the trigger point update page push element as a current page push element, using the trigger point update push form object as a trigger point push data source object, returning to perform trigger point page form service configuration based on the trigger point push data source object, the current page push element and the page reference resource, and obtaining a step of obtaining the trigger point update page form service until a second termination condition is met, so as to obtain current second difference information corresponding to the current trigger point.

And a substep S1209 of traversing each preset trigger point to obtain each current second difference information corresponding to each preset trigger point, comparing each current second difference information to obtain target second difference information, using the preset trigger point corresponding to the target second difference information as a target trigger point, and filling the current page push element into the page logic area according to the target trigger point to obtain a filling and typesetting source object.

In the substep S1210, a first initial page form service corresponding to the page initial trigger service is obtained, and the current page push element is filled into the page logic area based on the first initial page form service, so as to obtain a first initial filling and typesetting source object.

In the sub-step S1211, the third region information is determined based on the first start fill layout source object and the page layout source object.

And a substep S1212, adjusting the first initial page form service according to the third termination information, and returning to the step of filling the current page push element into the page logic region based on the first initial page form service to obtain the first starting filling and typesetting source object until the third termination information meets the third termination condition.

And a substep S1213 of taking the first initial page form service satisfying the third termination condition as the initial page form service.

And a substep S1214, selecting a candidate pushed data source object corresponding to the page start trigger service from the pushed data source object set of the current page push service according to the start page form service.

In another possible implementation manner, the page service to be updated is a non-page start triggering service. Thus, for step S120, in the process of acquiring the corresponding candidate pushed data source object based on the to-be-updated page service, the following exemplary sub-steps may be implemented, which are described in detail below.

In the sub-step S1215, a forward push data source object corresponding to a forward trigger service other than the page start trigger service is obtained, where the forward push data source object is a push data source object in a page push service corresponding to the forward trigger service.

And a substep S1216, regarding the forward pushed data source object as a candidate pushed data source object.

In a possible implementation manner, the service of the page to be updated is a page start triggering service.

Thus, for step S120, in the process of configuring the page form service based on the current page push element, the candidate pushed data source object and the page reference resource to obtain the updated page form service, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1217 of obtaining a second initial page form service corresponding to the page initial trigger service, and filling the current page push element and the candidate push data source object into the page logic area based on the second initial page form service to obtain an initial page configuration resource.

In sub-step S1218, fourth difference information is determined and obtained based on the starting page configuration resource and the page reference resource.

And a substep S1219 of adjusting the form parameters of each form region in the second initial page form service according to the fourth difference information, and returning to the step of filling the current page push element and the candidate push data source object into the page logic region based on the second initial page form service to obtain the initial page configuration resource until the fourth difference information satisfies a fourth termination condition, wherein the form parameters include a content refresh rate and a region size of the form region.

In the sub-step S1220, the second initial page form service meeting the fourth termination condition is used as an updated page form service corresponding to the page start trigger service.

In one possible implementation, the page service to be updated is a non-page-initiated trigger service.

And step S1221, acquiring a third initial page form service corresponding to the non-page initial trigger service, and filling a current page push element and a candidate push data source object into a page logic region according to the third initial page form service to obtain a non-initial page configuration resource.

And a substep S1222, determining to obtain fifth difference information based on the non-initial page configuration resource and the page reference resource, and obtaining a forward page form service corresponding to a forward trigger service of the non-page initial trigger service, where the forward page form service is a page form service of a page push service corresponding to the forward trigger service.

And a substep S1223 of determining posture difference information of the forward page form service and the third initial page form service, and obtaining target fifth difference information according to the fifth difference information and the posture difference information.

And substep S1224, adjusting a third initial page form service corresponding to the non-page-start trigger service according to the target fifth distinguishing information, and returning to the step of filling the current page push element and the candidate push data source object into the page logic area according to the third initial page form service to obtain the non-start page configuration resource until the target fifth distinguishing information meets a fifth termination condition.

And a substep S1225, taking the third initial page form service meeting the fifth termination condition as an updated page form service corresponding to the non-page start trigger service.

In one possible implementation manner, for step S130, in the process of selecting an updated push form object from the current page push service according to the updated page table form service, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S133, acquiring a preset number of data source elements in a data source object region of the page push service of the current page push service, determining the current data source elements from the preset number of data source elements, selecting an initial push page template tag from the current data source elements, and determining tag description information of the initial push page template tag.

And a substep S134, obtaining the template label description information, and performing relational operation according to the label description information and the template label description information to obtain the relational information.

And a substep S135, when the relationship information does not meet the preset relationship condition, returning to the step of selecting the initial pushed page template tag from the current data source element, and when the relationship information meets the preset relationship condition, taking the initial pushed page template tag meeting the preset relationship condition as a pushed page extension object corresponding to the current data source element.

And a substep S136, filling each pushed page extension object into a page logic area according to the updated page form service to obtain each extension subscription filling object.

And the substep S137 determines to obtain sixth distinguishing information based on each extended subscription filling object and the page data source object, compares the sixth distinguishing information corresponding to each extended subscription filling object to obtain target sixth distinguishing information, and uses the pushed page extension object corresponding to the target sixth distinguishing information as the updated pushed form object corresponding to the page data source object.

In a possible implementation manner, further to step S110, in the process of obtaining the portrait prediction result of each analyzable behavior process information in the page operation behavior big data uploaded by the blockchain financial exhibition terminal 300, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S111, when the page operation behavior big data is detected, extracting at least one analyzable behavior process information from the page operation behavior big data.

When the block chain center detects the page operation behavior big data through the network, it is clear that the portrait prediction needs to be carried out on the page operation behavior big data at present. At this time, the block chain center extracts the key behavior process information of the big data of the page operation behavior to obtain at least one piece of key behavior process information of the page. The page key behavior process information contains scene information capable of describing behavior segments in the page, and the portrait prediction result of the page segment can be obtained by performing portrait prediction on the page key behavior process information, so that the block chain center can use the extracted page key behavior process information as analyzable behavior process information. In other words, the at least one analyzable behavior process information is key behavior process information in the page operation behavior big data, for example, some key behavior process information with duration longer than a preset time in the operation process.

It is understood that the page operation behavior big data refers to operation behavior big data of a large number of users for various page objects in the exposed page. The page operation behavior big data may be operation behavior big data of an information query service, operation behavior big data of an information download service, and the like, and the embodiment of the present application is not limited herein.

It should be noted that, the block chain center may also extract analyzable behavior process information from the page to be tracked by using a preset key behavior process information extraction algorithm. The preset key behavior process information extraction algorithm can be set according to actual requirements, and the embodiment of the application is not limited herein.

And a substep S112, obtaining a preset behavior portrait prediction service set, and determining, for each analyzable behavior process information in the at least one analyzable behavior process information, a plurality of tracking analysis associated parameters corresponding to the preset behavior portrait prediction service set.

In this embodiment, after obtaining at least one piece of analyzable behavior process information, the block chain center first obtains a preset behavior portrait prediction service set, then determines each behavior portrait prediction service in the preset behavior portrait prediction service set, and tracks and analyzes associated parameters of the current analyzable behavior process information. Because the preset behavior portrait prediction service set comprises at least one behavior portrait prediction service based on the cloud computing container, the block chain center obtains a plurality of tracking analysis associated parameters aiming at the current analyzable behavior process information, and each behavior portrait prediction service has the tracking analysis associated parameters corresponding to the current analyzable behavior process information. Wherein the tracking analysis correlation parameter characterizes the analysis correlation (which can be understood as the importance degree) of the behavior portrait prediction service to the current analyzable behavior process information. That is, the plurality of tracking analysis related parameters are in one-to-one correspondence with each behavior portrait prediction service in the preset behavior portrait prediction service set.

After determining a plurality of tracking analysis associated parameters for the current analyzable behavior process information, the blockchain center will continue to use the next analyzable behavior process information as the current analyzable behavior process information until the blockchain center determines a plurality of tracking analysis associated parameters corresponding to each analyzable behavior process information. The preset behavior portrait prediction service set comprises at least one behavior portrait prediction service based on a cloud computing container, the behavior portrait prediction services are trained deep learning network models and can be used for predicting behavior portraits, and the behavior portrait prediction services can predict portraits from dimensions of different behavior portraits, so that comprehensiveness of portraits prediction is improved.

It should be noted that each of the plurality of tracking analysis related parameters represents an analysis relevance of each behavior portrait prediction service in the preset behavior portrait prediction service set to each analyzable behavior process information in the at least one analyzable behavior process information. Because the preset behavior portrait prediction service is centralized, some behavior portrait prediction services may not play a role in tracking and predicting the analyzable behavior process information. For example, the behavior portrait prediction service may not predict the production of portrait information for analyzable behavior process information, or the behavior portrait prediction service may have low coverage for tracking of analyzable behavior process information. The analytic relevance of these behavior image prediction services to the analyzable behavior process information is lower than the analytic relevance of behavior image prediction services that can generate correct predicted image information for analyzable behavior process information. Therefore, the block chain center needs to measure the analysis association degree of the behavior portrait prediction service to the analyzable behavior process information by using the tracking analysis association parameters, so as to select a suitable behavior portrait prediction service for the analyzable behavior process information in the following.

In a possible implementation manner, the block chain center may predict the tracking analysis associated parameters by using a trained preset tracking analysis associated analysis network model, and may also predict the tracking analysis associated parameters by using a preset prediction algorithm for the tracking analysis associated parameters, which is not limited herein.

It is understood that the tracking analysis related parameters may exist in the form of parameter numerical features, for example, the blockchain center identifies, for each analyzable behavior process information, a numerical feature of each behavior sketch prediction service, and the obtained parameter numerical features are the tracking analysis related parameters. The tracking analysis related parameters may also exist in the form of impact factor levels, for example, the blockchain center divides each behavior portrait prediction service into different impact factor levels for each analyzable behavior process information, for example, divides the levels with high impact factors and low impact factors, so as to obtain the tracking analysis related parameters.

And a substep S113 of determining a target behavior portrait prediction unit corresponding to each analyzable behavior process information from a preset behavior portrait prediction service set based on the plurality of tracking analysis associated parameters.

After obtaining a plurality of tracking analysis associated parameters of each analyzable behavior process information and reading each tracking analysis associated parameter, the block chain center determines which behavior portrait prediction services are important and which behavior portrait prediction services are unimportant for each analyzable behavior process information. Then, the blockchain center will select the behavior portrait prediction service important for each analyzable behavior process information as the target behavior portrait prediction unit corresponding to each analyzable behavior process information. After the target behavior image prediction unit is determined for all analyzable behavior process information at the center of the blockchain, at least one target behavior image prediction unit is obtained, wherein each analyzable behavior process information may correspond to one or more target behavior image prediction units.

In the embodiment of the present application, for a certain analyzable behavior process information, the blockchain center may select one important behavior portrait prediction service or may select a plurality of important behavior portrait prediction services, and each of the important behavior portrait prediction services is a target behavior portrait prediction unit of the analyzable behavior process information.

Furthermore, the number of target behavior representation prediction units corresponding to each analyzable behavior process information may be different, that is, the number of service calls included in a target behavior representation prediction unit corresponding to one analyzable behavior process information may be different from the number of service calls included in a target behavior representation prediction unit corresponding to another analyzable behavior process information. Accordingly, the total number of the at least one analyzable behavioral process information and the total number of service calls of the at least one target behavioral profile prediction unit may be different.

It should be noted that, although the total amount of behavior process information of at least one analyzable behavior process information is different from the total amount of at least one target behavior image prediction unit, each target behavior image prediction unit has analyzable behavior process information corresponding thereto because the target behavior image prediction unit is selected for each analyzable behavior process information, that is, each target behavior image prediction unit also corresponds to one or more analyzable behavior process information.

And a substep S114 of performing portrait prediction on each analyzable behavior process information by using a target behavior portrait prediction unit corresponding to each analyzable behavior process information to obtain a portrait prediction result of each analyzable behavior process information.

After the target behavior portrait prediction unit corresponding to each analyzable behavior process information is selected for each analyzable behavior process information, the block chain center can obtain a plurality of target behavior portrait prediction unit sets corresponding to each analyzable behavior process information respectively, and at this time, the plurality of target behavior portrait prediction unit sets can be used for portrait prediction of the corresponding analyzable behavior process information. At this time, the block chain center first selects at least one analyzable behavior process information corresponding to each target behavior image prediction unit (that is, each target behavior image prediction unit is included in a plurality of target behavior image prediction unit sets corresponding to the at least one analyzable behavior process information), then inputs the analyzable behavior process information corresponding to each target behavior image prediction unit into each target behavior image prediction unit for image prediction tracking, and uses the label output by each target behavior image prediction unit as the image prediction result of the analyzable behavior process information corresponding to each target behavior image prediction unit. After all target behavior portrait prediction units finish the identification tracking process, a portrait prediction result of each analyzable behavior process information is obtained, and the portrait prediction process aiming at page operation behavior big data is realized.

It should be noted that, since one or more behavior image prediction services may be provided in the target behavior image prediction unit corresponding to the analyzable behavior process information, some analyzable behavior process information that needs to be tracked and identified by the plurality of behavior image prediction services may exist, and at this time, only when all target behavior image prediction units, that is, all important behavior image prediction services for each analyzable behavior process information, complete the image prediction process, the block chain center may consider that the image prediction process is completed for each analyzable behavior process information.

In the embodiment of the application, at least one piece of analyzable behavior process information can be extracted from the page operation behavior big data, then the analysis association degree of each behavior portrait prediction service for each piece of analyzable behavior process information in the at least one piece of analyzable behavior process information is predicted, namely, tracking analysis association parameters are determined, and a proper target behavior portrait prediction unit is determined for each piece of analyzable behavior process information according to the tracking analysis association parameters, so that tracking recognition is performed on each piece of analyzable behavior process information only by using a target behavior portrait prediction unit which is important for each piece of analyzable behavior process information, the number of service calls for predicting each piece of analyzable behavior process information is reduced, and the portrait prediction efficiency is improved.

In one possible implementation manner, for step S113, in determining a target behavior representation prediction unit corresponding to each analyzable behavior process information from a preset behavior representation prediction service set based on a plurality of tracking analysis related parameters, the target behavior representation prediction unit may be determined by the following exemplary sub-steps, which are described in detail below.

The substep S1131 is to sort the plurality of tracking analysis related parameters of each analyzable behavior process information.

The plurality of tracking analysis associated parameters of each analyzable behavior process information represent the analysis association degree of each behavior portrait prediction service in the preset behavior portrait prediction service set to each analyzable behavior process information.

In sub-step S1132, the behavior portrait prediction service corresponding to the highest preset number of tracking analysis related parameters in the plurality of tracking analysis related parameters of each analyzable behavior process information is used as the target behavior portrait prediction unit corresponding to each analyzable behavior process information.

For example, if the preset number may be three, the behavior representation prediction service corresponding to the top three tracking analysis related parameters of the plurality of tracking analysis related parameters of each analyzable behavior process information may be used as the target behavior representation prediction unit corresponding to each analyzable behavior process information.

In this embodiment, the behavior portrait prediction service corresponding to the highest preset number of tracking analysis associated parameters in the plurality of tracking analysis associated parameters of each analyzable behavior process information is used as the target behavior portrait prediction unit corresponding to each analyzable behavior process information, so that the behavior portrait prediction service with the importance degree satisfying a certain range can be used for subsequent portrait prediction.

In another possible implementation, each behavior profile prediction service in the preset set of behavior profile prediction services may have a tracking analysis correlation parameter corresponding to at least one analyzable behavior process information.

Then, still referring to step S113, in determining a target behavior representation prediction unit corresponding to each analyzable behavior process information from a preset behavior representation prediction service set based on a plurality of tracking analysis associated parameters, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S1133, comparing the tracking analysis associated parameter corresponding to each analyzable behavior process information in the plurality of tracking analysis associated parameters with a preset association threshold to obtain a comparison result.

For example, the correlation threshold may be set according to actual design requirements, and is not limited in detail herein.

And a substep S1134 of selecting the behavior portrait prediction service with the comparison result representing, tracking and analyzing the correlation parameter larger than the preset correlation threshold from the preset behavior portrait prediction service set as a target behavior portrait prediction unit corresponding to each analyzable behavior process information.

In this embodiment, the behavior portrait prediction service with the tracking analysis correlation parameter larger than the preset correlation threshold is used as the target behavior portrait prediction unit corresponding to each analyzable behavior process information, so that the behavior portrait prediction service with the importance degree meeting a certain range can be used for subsequent portrait prediction.

In one possible implementation manner, regarding step S114, in the process of performing portrait prediction on each analyzable behavior process information by using a target behavior portrait prediction unit corresponding to each analyzable behavior process information to obtain a portrait prediction result of each analyzable behavior process information, the following exemplary sub-steps may be implemented, which are described in detail below.

In sub-step S1141, analyzable behavior process information corresponding to each target behavior sketch prediction unit is determined as target behavior process information.

And a substep S1142 of counting the overlay service of the target behavior process information of each target behavior portrait prediction unit to obtain the target overlay service position of each target behavior portrait prediction unit.

In this embodiment, the overlay service may refer to an overlay page display service (e.g., services of various query interfaces in a query service), and a target overlay service position of each target behavior representation prediction unit may be obtained.

And a substep S1143 of determining a corresponding predicted node position for each target behavior sketch prediction unit by using the target overlay service position.

In this embodiment, for each target overlay service position, the corresponding overlay service is different, so that a corresponding predicted node position can be determined for each target behavior representation predicting unit, so that the predicted node position corresponds to or is associated with each target overlay service position.

In the substep S1144, the target behavior process information corresponding to each target behavior picture predicting unit is subjected to picture prediction by each target behavior picture predicting unit according to the predicted node position, and a picture prediction result corresponding to the target behavior process information is obtained.

In the sub-step S1145, after the image prediction is performed on the target behavior process information corresponding to each target behavior image prediction unit, an image prediction result corresponding to each analyzable behavior process information is obtained.

In a possible implementation manner, for the sub-step S1144, the predicted node position may be specifically compared with a preset node position to obtain a node position comparison result.

In this embodiment, the node position comparison result represents a time sequence relationship between the predicted node position and the preset node position, for example, a front-back relationship of the time sequence.

On the basis, when the node position comparison result represents that the predicted node position is before the preset node position, each target behavior portrait prediction unit is used for carrying out portrait prediction on target behavior process information corresponding to each target behavior portrait prediction unit, and a portrait prediction result corresponding to the target behavior process information is obtained.

For another example, when the node position comparison result indicates that the predicted node position is behind the preset node position, the image prediction of the target behavior process information corresponding to each target behavior image prediction unit by each target behavior image prediction unit is finished.

For another example, when the image prediction of the current target behavior process information corresponding to the current target behavior image prediction unit is completed by using the current target behavior image prediction unit corresponding to the current node position, the current tracking completion time corresponding to the current target behavior image prediction unit is obtained. The current node position is any one of the predicted node positions corresponding to each target behavior profile prediction unit.

For another example, when the current tracking completion time is greater than or equal to the preset maximum time, the next target behavior portrait prediction unit corresponding to the next node position of the current node position is stopped to perform portrait prediction on the target behavior process information corresponding to the next target behavior portrait prediction unit.

In a possible implementation manner, further to step S112, in the process of determining, for each analyzable behavior process information of the at least one analyzable behavior process information, a plurality of tracking analysis associated parameters corresponding to the preset behavior portrait prediction service set, the following exemplary sub-steps may be implemented, which are described in detail below.

In the sub-step S1121, for each analyzable behavior process information, a tracking analysis association analysis network model is used to predict a tracking analysis association parameter corresponding to each behavior portrait prediction service in the preset behavior portrait prediction service set.

In the substep S1122, when the prediction of the tracking analysis associated parameters is completed for all the preset behavior portrait prediction service sets, a plurality of tracking analysis associated parameters corresponding to the preset behavior portrait prediction service sets are obtained.

As an alternative example, the preset tracking analysis association analysis network model is obtained by training through the following steps:

step S1101, acquiring an initial analysis network model, past behavior process information, a plurality of past portrait prediction data, a plurality of portrait service influence information corresponding to the plurality of past portrait prediction data, and a prediction overlay service corresponding to a preset behavior portrait prediction service set, where the past behavior process information is behavior process information for performing portrait prediction at a past time by using the preset behavior portrait prediction service set, the plurality of past portrait prediction data is portrait prediction data obtained by performing portrait prediction on the past behavior process information by using the preset behavior portrait prediction service set, and the plurality of past portrait prediction data and the plurality of portrait service influence information are in one-to-one correspondence. The predicted overlay service comprises the overlay service of each behavior portrait prediction service on the information of the past behavior process.

Step S1102 is to construct a plurality of training tracking analysis association parameters corresponding to a preset behavior portrait prediction service set according to past behavior process information by using a plurality of past portrait prediction data, a plurality of portrait service influence information, and a prediction overlay service. And each training tracking analysis associated parameter in the plurality of training tracking analysis associated parameters corresponds to each behavior portrait prediction service in a preset behavior portrait prediction service set one by one.

Step S1103, training the initial analysis network model by using the past behavior process information and a plurality of training tracking analysis association parameters until the iteration end requirement is met, and obtaining a preset tracking analysis association analysis network model.

Wherein each of the plurality of past image prediction data includes an image prediction reliability and an image prediction coverage.

It is to be noted that, in the above step S1102, the following sub-steps may be implemented.

In the substep S11021, for the current behavior portrait prediction service in the preset behavior portrait prediction service set, current past portrait prediction data is extracted from a plurality of past portrait prediction data, and current overlay service is extracted from the predicted overlay service. The current behavior portrait prediction service is any one of the preset behavior portrait prediction service sets.

And a substep S11022 of constructing training tracking analysis related parameters corresponding to the current behavior portrait prediction service by using the influence information of the current portrait service corresponding to the current past portrait prediction data, the current portrait prediction coverage of the current past portrait prediction data, the current portrait prediction credibility and the current coverage service.

In the substep S11023, when corresponding training tracking analysis associated parameters are constructed for the preset behavior portrait prediction service set, a plurality of training tracking analysis associated parameters are obtained.

In a possible implementation manner, after the step S1103 and before the sub-step S1121, the following steps may be further included:

and step S1104, predicting a plurality of test tracing analysis associated parameters corresponding to the preset behavior portrait prediction service set by using a preset tracing analysis associated analysis network model according to the test behavior process information, and sequencing the plurality of test tracing analysis associated parameters to obtain analysis associated parameter distribution.

Step S1105, obtaining the distribution of the preset analysis associated parameters corresponding to the test behavior process information. The preset analysis association parameter distribution represents the real sequencing of the analysis association degree of each behavior portrait prediction service in the preset behavior portrait prediction service set on the test behavior process information.

Step S1106, determining the predicted coverage of the preset tracking analysis correlation analysis network model according to the analysis correlation parameter distribution and the preset analysis correlation parameter distribution.

Step S1107, when the predicted coverage is smaller than a preset coverage threshold, the initial analysis network model is trained by reusing past behavior process information and a plurality of training tracking analysis association parameters until the iteration end requirement is met, and the latest tracking analysis association analysis network model is obtained.

Accordingly, based on the above description, in sub-step S1121, for each analyzable behavior process information, a tracking analysis associated parameter corresponding to each behavior portrait prediction service in the preset behavior portrait prediction service set may be predicted by using the latest tracking analysis associated analysis network model.

In a possible implementation manner, the preset trace analysis association parsing network model may include: the device comprises an input layer, at least two convolution layers, at least two pooling layers, a dimension reduction layer, a full connection layer and an output layer. The output of the input layer is connected with the input of the at least two convolution layers, the at least two convolution layers and the at least two pooling layers are sequentially and alternately connected, the input of the dimensionality reduction layer is connected with the output of the at least two pooling layers, the input of the full-connection layer is connected with the output of the dimensionality reduction layer, and the input of the output layer is connected with the output of the full-connection layer.

Each analyzable behavior process information enters at least two convolutional layers through an input layer, at least two pooling layers are used for receiving feature maps output by the at least two convolutional layers, a dimensionality reduction layer is used for receiving the pooling feature map output by the last pooling layer of the at least two pooling layers, a full-connection layer is used for receiving dimensionality reduction data output by the dimensionality reduction layer, and an output layer is used for receiving processing data output by the full-connection layer and outputting tracking analysis associated parameters corresponding to each behavior portrait prediction service;

wherein the at least two convolutional layers comprise: the number of channels of the first coiling layer is different from that of the second coiling layer; the at least two pooling layers include: a first pooling layer, a second pooling layer; the output of the input layer is connected with the input of the first convolution layer, the input of the first pooling layer is connected with the output of the first convolution layer, the input of the second convolution layer is connected with the output of the first pooling layer, the input of the second pooling layer is connected with the output of the second convolution layer, and the input of the dimension reduction layer is connected with the output of the second pooling layer.

Further, in a possible implementation manner, on the basis of the above steps, in order to perform content-specific update on a page in combination with the above portrait prediction result of each piece of analyzable behavior process information, so as to enable a user to obtain a better content service experience, the method provided in the embodiment of the present application may further include the following steps:

in step S1150, the image prediction result based on each analyzable behavior process information is used to perform page update on the operation page that can be displayed later by the blockchain financial display terminal 300, so as to obtain a page update result.

In detail, in an alternative embodiment, the step S1150 may be implemented by the following exemplary sub-steps, which are described in detail below.

Sub-step S1151, obtaining a portrait mapping page element corresponding to a portrait prediction result of each analyzable behavioral process information, the portrait mapping page element being generated according to at least one page element function associated with the portrait prediction result, the portrait mapping page element having a portrait tag corresponding thereto.

In the sub-step S1152, the portrait mapping page element is analyzed to obtain at least one page update item having a page update mode, where the page update item corresponds to the page element function one to one, and the at least one page update item includes an initial update position.

And a substep S1153 of sequentially updating at least one page update item according to the updated page content of the portrait tag corresponding to the updated page update item from the initial update position, wherein for the currently updated target page update item, the page content arrangement corresponding to the target page update item is updated according to the updated page content of the portrait tag of the target page update item.

In the substep S1154, if the target page update item is a page update item of the extended display tag or a page update item of the subscription display tag, directly configuring the update page content of the portrait tag corresponding to the target page update item to the next display page of the target page update item.

In step S1155, when the target page update item is a lower page update item, the updated page content of the portrait tag corresponding to the target page update item is arranged.

Hereinafter, the description will be made with reference to specific examples.

(1) When the page updating item of the subscription display tag is updated, according to a subscription content template supported by the subscription service indicated by the subscription display field in the page updating item of the subscription display tag, the updated page content of the portrait tag is converted into data corresponding to the subscription content template to obtain conversion data, the conversion data is transmitted to the subscription service through the subscription content template supported by the subscription service, the processing result of the subscription service on the conversion data is obtained, the updated page content of the page updating item of the subscription display tag is generated based on the processing result, and then the next round of display pages of the target page updating item is configured.

(2) When the page updating item of the carousel display tag is updated, acquiring a carousel starting sub-page updating item, a carousel structure sub-page updating item and a carousel ending sub-page updating item which are obtained by analyzing the page updating item of the carousel display tag. And if the update item of the carousel starting sub-page is determined to meet the carousel condition, sending the update page content of the carousel starting sub-page update item to the carousel structure sub-page update item, and if the update item of the carousel starting sub-page is determined to not meet the carousel condition, sending the update page content of the carousel starting sub-page update item to the carousel ending sub-page update item. And after updating the carousel structure sub-page updating item, sending the updating page content of the carousel structure sub-page updating item to a carousel ending sub-page updating item for processing. And if the carousel is determined to be finished, the updated page content of the carousel-finished sub-page updating item is used as the updated page content of the page updating item of the carousel display tag.

(3) When the page updating item of the fixed-point display tag is updated, a fixed-point display starting sub-page updating item, a fixed-point display structure sub-page updating item and a fixed-point display ending sub-page updating item which are obtained by analyzing the page updating item of the fixed-point display tag are obtained. And if the fixed point display starting sub-page updating item is determined to meet the fixed point display condition, sending the updating page content of the fixed point display starting sub-page updating item to the fixed point display structure sub-page updating item, and if the fixed point display condition is determined not to be met, sending the updating page content of the fixed point display starting sub-page updating item to the fixed point display ending sub-page updating item. And after the fixed point display structure sub-page updating item is updated, sending the updated page content of the fixed point display structure sub-page updating item to the fixed point display end sub-page updating item for processing. And updating the fixed point display ending sub-page updating item, and taking the updated page content of the fixed point display ending sub-page updating item as the updated page content of the page updating item of the fixed point display tag.

(4) When the page updating item of the synchronous display tag is updated, the synchronous starting sub-page updating item, the synchronous processing sub-page updating item and the synchronous ending sub-page updating item which are obtained by analyzing the page updating item of the synchronous display tag are obtained. Updating a synchronous start sub-page updating item, sending the updated page content of the synchronous start sub-page updating item to a synchronous processing sub-page updating item, sending the updated page content of the synchronous processing sub-page updating item to a synchronous end sub-page updating item for processing after the synchronous processing sub-page updating item is updated, updating the synchronous end sub-page updating item to integrate the updated page content of the synchronous processing sub-page updating item, and taking the integrated data as the updated page content of the page updating item of the synchronous display tag.

Fig. 3 is a schematic diagram of functional modules of a page service processing apparatus 300 based on cloud computing and artificial intelligence according to an embodiment of the present disclosure, in this embodiment, functional modules of the page service processing apparatus 300 based on cloud computing and artificial intelligence may be divided according to a method embodiment executed by the blockchain center 100, that is, the following functional modules corresponding to the page service processing apparatus 300 based on cloud computing and artificial intelligence may be used to execute each method embodiment executed by the blockchain center 100. The cloud computing and artificial intelligence based page service processing apparatus 300 may include a first configuration module 310, a second configuration module 320, an update module 330, and a third configuration module 340, and the functions of the functional modules of the cloud computing and artificial intelligence based page service processing apparatus 300 are described in detail below.

The first configuration module 310 is configured to obtain a portrait prediction result of each analyzable behavior process information in the page operation behavior big data uploaded by the block chain financial display terminal 300, and configure a page reference resource of a page service to be updated according to the portrait prediction result of each analyzable behavior process information, where the page reference resource includes a page layout source object and a page data source object, where the portrait prediction result of each analyzable behavior process information is a portrait prediction result obtained by at least one behavior portrait prediction service based on a cloud computing container, and the behavior portrait prediction service is obtained based on artificial intelligence model training. The first configuration module 310 may be configured to perform the step S110, and the detailed implementation of the first configuration module 310 may refer to the detailed description of the step S110.

The second configuration module 320 is configured to select a current page push element from a current page push service corresponding to the to-be-updated page service, obtain a corresponding candidate pushed data source object based on the to-be-updated page service, and perform page form service configuration based on the current page push element, the candidate pushed data source object, and the page reference resource, so as to obtain an updated page form service. The second configuration module 320 may be configured to perform the step S120, and the detailed implementation of the second configuration module 320 may refer to the detailed description of the step S120.

The updating module 330 is configured to select an updated push form object from the current page push service according to an updated page form service, determine an updated page source data service corresponding to the current page push service according to the updated push form object and the current page push element, perform form binding on the updated push form object and the current page push element based on the updated page form service to obtain a page configuration resource, update the current page push element and a candidate push data source object according to first difference information between the page configuration resource and the page reference resource, and return to the step of page form service configuration until a first termination condition is met. The updating module 330 may be configured to perform the step S130, and the detailed implementation of the updating module 330 may refer to the detailed description of the step S130.

The third configuration module 340 is configured to perform page push service configuration based on the updated page source data service and the updated page table single service that satisfy the first termination condition, so as to obtain a target page push service corresponding to the page service to be updated. The third configuration module 340 may be configured to perform the step S140, and the detailed implementation manner of the third configuration module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the first configuration module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the first configuration 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 blockchain center 100 for implementing the cloud computing and artificial intelligence based page service processing method according to the embodiment of the present disclosure, and as shown in fig. 4, the blockchain center 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, the at least one processor 110 executes the determining machine execution instruction stored in the machine-readable storage medium 120 (for example, the first configuration module 310, the second configuration module 320, the update module 330, and the third configuration module 340 included in the cloud computing and artificial intelligence based page service processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the cloud computing and artificial intelligence based page service processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned block chain financial presentation terminal 200.

For a specific implementation process of the processor 110, reference may be made to the various embodiments of the method executed by the blockchain center 100, which have similar implementation principles and technical effects, and further description of the embodiments 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, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, an embodiment of the present application further provides a readable storage medium, where the readable storage medium stores a determining machine execution instruction, and when a processor executes the determining machine execution instruction, the above page service processing method based on cloud computing and artificial intelligence is implemented.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, particular push elements are used in this description to describe embodiments of this description. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be embodied as a computer program product, comprising computer readable program code, embodied in one or more computer readable media.

The determining machine storage medium may comprise a propagated data signal with the determining machine program code embodied therein, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A determining machine storage medium can be any determining machine readable medium in addition to a determining machine readable storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

The deterministic machine program code required for the operation of the various components of this specification can be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a passive programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may run entirely on the user-determination machine, or as a separate indexing sequence on the user-determination machine, or partly on the user-determination machine, partly on the remote-determination machine, or entirely on the remote-determination machine or server. In the latter case, the remote determination machine may be connected to the user determination machine through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or to an external determination machine (e.g., through the internet), or in a cloud computing environment, or as a service using, for example, software as a service (SaaS).

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. A page service processing method based on cloud computing and artificial intelligence is applied to a blockchain center which is in communication connection with a plurality of blockchain financial display terminals, and comprises the following steps:

2. The cloud computing and artificial intelligence based page service processing method according to claim 1, wherein the step of updating the current page push element and the candidate push data source object according to the first difference information between the page configuration resource and the page reference resource and returning the page form service configuration until a first termination condition is satisfied includes:

3. The cloud computing and artificial intelligence based page service processing method according to claim 2, wherein:

4. The cloud computing and artificial intelligence based page service processing method according to claim 3, wherein the page service to be updated is a page start trigger service;

5. The cloud computing and artificial intelligence based page service processing method according to claim 1, wherein the page service to be updated is a non-page start trigger service;

6. The cloud computing and artificial intelligence based page service processing method according to claim 1, wherein the page service to be updated is a page start trigger service;

7. The cloud computing and artificial intelligence based page service processing method according to claim 1, wherein the page service to be updated is a non-page start trigger service;

8. The cloud computing and artificial intelligence based page service processing method according to any one of claims 1 to 7, wherein the selecting an updated push form object from the current page push services according to an updated page table form service includes:

9. The cloud computing and artificial intelligence based page service processing method according to any one of claims 1 to 8, wherein the step of obtaining the portrait prediction result of each analyzable behavior process information in the page operation behavior big data uploaded by the blockchain financial presentation terminal includes:

10. A blockchain center 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 through a bus system, the network interface is configured to be communicatively connected to at least one blockchain financial presentation terminal, the machine-readable storage medium is configured to store a program, instructions, or code, and the processor is configured to execute the program, instructions, or code in the machine-readable storage medium to perform the cloud computing and artificial intelligence based page service processing method of any one of claims 1 to 9.