CN112671885B

CN112671885B - Information analysis method based on cloud computing and big data and digital financial service platform

Info

Publication number: CN112671885B
Application number: CN202011516460.1A
Authority: CN
Inventors: 刚倩
Original assignee: Shanghai Lumaotong Industrial Group Co ltd
Current assignee: Shanghai lumaotong Industrial Group Co.,Ltd.
Priority date: 2020-12-21
Filing date: 2020-12-21
Publication date: 2021-09-14
Anticipated expiration: 2040-12-21
Also published as: CN112671885A; CN113596121A; CN113596120A

Abstract

The embodiment of the application provides an information analysis method based on cloud computing and big data and a digital financial service platform, wherein the method comprises the steps of determining the state of an interactive element corresponding to an interaction initiating object presented by a second tracking data segment by adopting a first data association degree between the first tracking data segment and the second tracking data segment in a target tracking data stream, and under the condition that the state of the interactive element corresponding to the interaction initiating object indicates that a changed target interactive element exists, reducing the number of the tracking data segments required to be sent by only sending the second tracking data segment indicating that the changed target interactive element exists, and enabling the changed target interactive element in the sent tracking data segment to be more convenient for data mining, so that the load pressure of data communication is reduced, and the calculation amount of data mining is reduced.

Description

Information analysis method based on cloud computing and big data and digital financial service platform

Technical Field

The application relates to the technical field of information analysis, in particular to an information analysis method and a digital financial service platform based on cloud computing and big data.

Background

Today, most of the application programs of the intelligent terminals provide a message pushing function, such as hot news recommendation of news clients, chat message reminding of chat interactive tools, e-commerce product promotion information, notification and approval processes of enterprise applications and the like. The information push plays an important role in improving the activity of products, the utilization rate of functional modules, the viscosity of users and the retention rate of users, and the information push is used as a key channel in application program operation and can effectively promote the realization of targets for the reasonable application of the information push.

In the related art, in the process of data mining and information pushing between different users in an application session process page, interaction conditions of the different users in target interaction operation data for pushing information need to be continuously analyzed, however, currently, a tracking data segment recorded in real time is uploaded to a cloud computing container on a cloud computing platform for data mining, so that load pressure of data communication is increased, and the calculation amount of data mining is greatly increased.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present application aims to provide an information parsing method and a digital financial service platform based on cloud computing and big data, which determine a state of an interactive element corresponding to an interaction initiation object presented by a second trace data segment by using a first data association degree between the first trace data segment and the second trace data segment in a target trace data stream, in case that the state of the interactive element corresponding to the interaction initiating object indicates that there is a changed target interactive element, by transmitting only the second piece of trace data indicating that there is a changed target interaction element, the number of pieces of trace data that need to be transmitted is reduced, and the target interaction elements with changes in the sent tracking data segments are more convenient for data mining, so that the load pressure of data communication is reduced, and the calculation amount of data mining is reduced.

In a first aspect, the present application provides an information parsing method based on cloud computing and big data, which is applied to a digital financial service platform, where the digital financial service platform is in communication connection with a plurality of information display devices, and the method includes:

obtaining an interest portrait between a target session service object and a corresponding associated session service object, and sending push information to information display equipment corresponding to the target session service object and the associated session service object based on the interest portrait, wherein the interest portrait between the target session service object and the corresponding associated session service object is obtained based on a big data session node sequence between the target session service object and the associated session service object in a target session process page;

acquiring target interaction operation data of the target session service object and the associated session service object on push information, and acquiring a target tracking data stream obtained by tracking an interaction object in a target interaction service according to the target interaction operation data, wherein the interaction object in the target interaction service comprises an interaction initiating object and an interaction element corresponding to the interaction initiating object;

determining a first trace data segment and a second trace data segment to be processed from the target trace data stream, and acquiring a first data association degree between the first trace data segment and the second trace data segment, wherein a trace node of the first trace data segment appearing in the target trace data stream is prior to the second trace data segment;

and determining the state of the interactive element corresponding to the interactive initiation object, which is presented by the second tracking data segment, according to the first data association degree, and sending the second tracking data segment to a cloud computing container for data mining under the condition that the state of the interactive element corresponding to the interactive initiation object indicates that a changed target interactive element exists, wherein the cloud computing container is used for performing data mining on the process that the interactive element changes at the interactive initiation object.

In a possible implementation manner of the first aspect, the step of obtaining the first data association degree between the first trace data segment and the second trace data segment includes:

comparing the data characteristic segment of the first tracking data segment with the data characteristic segment of the second tracking data segment to obtain the association degree of the first tracking data segment;

and obtaining a loss degree between a contrast association degree and the association degree of the first tracking data segment as the first data association degree, wherein the contrast association degree is a second tracking data segment association degree between a third tracking data segment and a fourth tracking data segment in the target tracking data stream, and the third tracking data segment and the fourth tracking data segment are tracking data segments in a target tracking node segment recorded at the beginning of the target tracking data stream.

In a possible implementation manner of the first aspect, the step of determining, according to the first data association degree, a state of an interaction element corresponding to the interaction initiation object, which is presented by the second tracking data segment, includes:

obtaining an interactive token corresponding to the target tracking data stream, wherein the interactive token comprises: the interactive system comprises a first interactive mark used for indicating that an interactive element corresponding to the interactive initiating object is positioned at a first interactive process node and a second interactive mark used for indicating that an interactive element corresponding to the interactive initiating object is positioned at a second interactive process node, wherein the switching quantity of the interactive element corresponding to the interactive initiating object under the second interactive process node is greater than that of the interactive element corresponding to the interactive initiating object under the first interactive process node;

when the first data association degree is less than or equal to a first association degree and the interaction mark is a first interaction mark, determining that the state of an interaction element corresponding to the interaction initiating object is a switching state, wherein the switching state is used for indicating that the target interaction element exists in the interaction process of the interaction initiating object;

determining that the state of the interaction element corresponding to the interaction initiating object is the switching state when the first data association degree is greater than or equal to a second association degree and the interaction mark is a second interaction mark;

when the interaction mark is the first interaction mark and the first data association degree is smaller than the first association degree, the second tracking data segment is sent to the cloud computing container and then the first interaction mark is deleted;

when the interactive mark is the second interactive mark and the first data association degree is greater than the first association degree and less than the second association degree, converting the second interactive mark into the first interactive mark;

and deleting the second interactive mark when the interactive mark is the second interactive mark and the first data association degree is smaller than the first association degree.

In a possible implementation manner of the first aspect, before the acquiring the interactive mark corresponding to the target tracking data stream, the method further includes:

detecting whether the target tracking data stream is configured with the interactive mark;

determining a second degree of data association between a fifth piece of trace data and a sixth piece of trace data in the target trace data stream in the event that the target trace data stream is detected to be unconfigured with the interactive mark, wherein the fifth piece of trace data appears in the target trace data stream at a trace node prior to the sixth piece of trace data;

generating the first interaction mark when the second data association degree is greater than the first association degree and less than or equal to the second association degree;

and generating the second interaction mark under the condition that the second data association degree is greater than the second association degree.

In a possible implementation manner of the first aspect, in a case that a state of an interaction element corresponding to the interaction initiating object indicates that there is a target interaction element that changes, the step of sending the second tracking data segment to the cloud computing container includes:

adding the second tracking data segment into a tracking data segment uploading queue under the condition that the state of the interaction element corresponding to the interaction initiating object indicates that a changed target interaction element exists;

when the number of the tracking data segments in the tracking data segment uploading queue is smaller than a first number, sequentially transmitting the tracking data segments in the tracking data segment uploading queue to the cloud computing container in sequence;

and under the condition that the number of the tracking data segments in the tracking data segment uploading queue is greater than or equal to the first number, aggregating the first x tracking data segments in the tracking data segment uploading queue into a tracking data segment data packet, and sending the tracking data segment data packet to the cloud computing container, wherein the tracking data segments in the tracking data segment uploading queue are arranged according to the sequence of the tracking nodes in the target tracking data stream.

In a possible implementation manner of the first aspect, the step of determining a first trace data segment and a second trace data segment to be processed from the target trace data stream includes:

selecting a first trace data segment and a second trace data segment from the target trace data stream, wherein a trace node of the first trace data segment appearing in the target trace data stream is prior to the second trace data segment;

determining the first trace data segment as the first trace data segment;

determining the second piece of trace data as the second piece of trace data; or

segmenting the first tracking data segment to obtain a first segmented tracking data segment sequence, and segmenting the second tracking data segment to obtain a second segmented tracking data segment sequence;

and taking the w-th segmentation trace data segment in the first segmentation trace data segment sequence as the first trace data segment, and taking the w-th segmentation trace data segment in the second segmentation trace data segment sequence as the second trace data segment.

In a possible implementation manner of the first aspect, the step of obtaining an interest representation between a target session service object and a corresponding associated session service object, and sending push information to an information display device corresponding to the target session service object and the associated session service object based on the interest representation includes:

acquiring a big data session node sequence between a target session service object and an associated session service object in a target session process page, wherein the big data session node sequence comprises a plurality of target session nodes called by the target session service object in the target session process page in a target session subscription service, a plurality of associated session nodes called by the associated session service object in the target session process page in the target session subscription service, and calling service positions of the session nodes, and the target session process page is initiated and calls computing resources to perform computing operation based on an edge side of the digital financial service platform;

constructing a session node intention list by utilizing a target session node intention list corresponding to the target session nodes and associated session node intention lists corresponding to the associated session nodes, and acquiring session node intention description information according to the session node intention list, wherein the target session node intention list is used for representing key intention label distribution of the target session nodes interacted according to the calling service position, the associated session node intention list is used for representing key intention label distribution of the associated session nodes interacted according to the calling service position, and the session node intention description information is used for representing intention interest degrees of the target session node intention list and the associated session node intention lists;

constructing a session node interest sequence by utilizing the target session node and the associated session node which are called in the target session subscription service segment in the big data session node sequence and according to the service flow direction of the calling service position, and acquiring session node interest description information according to the session node interest sequence, wherein the session node interest description information is used for representing the intention interest degree between at least two mapping session nodes in the session node interest sequence;

obtaining the session interest degree between the target session service object and the associated session service object according to the session node intention description information and the session node interest description information, determining an interest portrait between the target session service object and the associated session service object according to the session interest degree, and sending push information to information display equipment corresponding to the target session service object and the associated session service object based on the interest portrait.

In a possible implementation manner of the first aspect, the step of constructing a session node interest sequence by using the target session node and the associated session node that are called in the target session subscription service segment and in the service flow direction of the calling service location in the big data session node sequence includes:

carrying out calling service positioning on the target session node and the associated session node in the target session subscription service segment in the big data session node sequence to obtain a plurality of calling service positioning information;

and according to the calling service position, carrying out service flow direction indexing on the calling service positioning information, and recording a session content relation between a target session node and an associated session node corresponding to the matched service flow direction relation according to a service flow direction indexing result so as to construct a session node interest sequence.

In a possible implementation manner of the first aspect, the sending, based on the interest representation, push information to an information presentation device corresponding to the target session service object and the associated session service object includes:

acquiring an portrait label push page of the interest portrait and corresponding to each to-be-associated index push page;

respectively carrying out page entry extraction on the portrait label push page and each to-be-associated index push page to obtain a portrait label push page entry sequence and each to-be-associated index push page entry sequence, and calculating the subject association degree of the portrait label push page entry sequence and each to-be-associated index push page entry sequence to obtain each subject matching feature;

performing interface service matching on the portrait label push page and each index push page to be associated to obtain each interface service matching characteristic;

calculating the portrait label push page and each page knowledge graph corresponding to each index push page to be associated, and calculating to obtain each page knowledge distribution characteristic based on the page knowledge graphs;

calculating the correlation degree of the portrait label push page and each index push page to be correlated respectively based on the interface service matching feature, the theme matching feature and the page knowledge distribution feature;

obtaining a target index pushing page of each to-be-associated index pushing page according to the correlation degree between the portrait label pushing page and each to-be-associated index pushing page;

sending the portrait label push page and the target index push page as the push information to the target session service object and the information display equipment corresponding to the associated session service object

In a second aspect, an embodiment of the present application further provides an information analysis device based on cloud computing and big data, which is applied to a digital financial service platform, where the digital financial service platform is in communication connection with a plurality of information display devices, and the device includes:

the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring an interest portrait between a target session service object and a corresponding associated session service object and sending push information to information display equipment corresponding to the target session service object and the associated session service object based on the interest portrait, and the interest portrait between the target session service object and the corresponding associated session service object is acquired based on a big data session node sequence between the target session service object and the associated session service object in a target session process page;

the second obtaining module is used for obtaining target interaction operation data of the target session service object and the associated session service object for the push information, and obtaining a target tracking data stream obtained by tracking an interaction object in the target interaction service according to the target interaction operation data, wherein the interaction object in the target interaction service comprises an interaction initiating object and an interaction element corresponding to the interaction initiating object;

the determining module is used for determining a first trace data segment and a second trace data segment to be processed from the target trace data stream and acquiring a first data association degree between the first trace data segment and the second trace data segment, wherein a trace node of the first trace data segment in the target trace data stream is prior to the second trace data segment;

and the sending module is used for determining the state of the interactive element corresponding to the interactive initiating object, which is presented by the second tracking data segment, according to the first data association degree, and sending the second tracking data segment to a cloud computing container for data mining under the condition that the state of the interactive element corresponding to the interactive initiating object indicates that a changed target interactive element exists, wherein the cloud computing container is used for performing data mining on the process that the interactive element changes at the interactive initiating object.

In a third aspect, an embodiment of the present application further provides an information parsing system based on cloud computing and big data, where the information parsing system based on cloud computing and big data includes a digital financial service platform and a plurality of information display devices communicatively connected to the digital financial service platform;

the digital financial service platform is used for:

In a fourth aspect, an embodiment of the present application further provides a digital financial service platform, where the digital financial service platform 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 information presentation device, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the cloud computing and big data based information parsing method in the first aspect or any one of possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer is caused to execute the cloud computing and big data based information parsing 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 and the device determine the state of the interactive element corresponding to the interaction initiating object, which is presented by the second trace data segment, according to the first data association degree between the first trace data segment and the second trace data segment in the target trace data stream, and when the state of the interactive element corresponding to the interaction initiating object indicates that the changed target interactive element exists, only the second trace data segment indicating that the changed target interactive element exists is sent, so that the number of the trace data segments to be sent is reduced, and the sent trace data segment has the changed target interactive element, which is more convenient for data mining, so that the load pressure of data communication is reduced, and the calculation amount of data mining is reduced.

Drawings

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

Fig. 1 is a schematic view of an application scenario of an information analysis system based on cloud computing and big data according to an embodiment of the present application;

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

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

fig. 4 is a schematic block diagram of structural components of a digital financial service platform for implementing the cloud computing and big data based information parsing method according to an embodiment of the present application.

Detailed Description

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

Fig. 1 is an interaction diagram of an information parsing system 10 based on cloud computing and big data according to an embodiment of the present application. The cloud computing and big data based information parsing system 10 may include a digital financial services platform 100 and an information presentation apparatus 200 communicatively connected with the digital financial services platform 100. The cloud computing and big data based information parsing system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the cloud computing and big data based information parsing system 10 may also include only a part 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 digital financial service platform 100 provided by the present application may be applied to scenes such as smart medical, smart city management, smart industrial internet, general service monitoring management, etc. in which a big data technology or a cloud computing technology may be applied, and for example, may also be applied to scenes such as but not limited to new energy automobile system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud automobile management platform, block chain financial data service platform, etc., but is not limited thereto.

In this embodiment, the digital financial service platform 100 and the information presentation device 200 in the cloud computing and big data based information analysis system 10 may cooperatively perform the cloud computing and big data based information analysis method described in the following method embodiment, and the detailed description of the following method embodiment may be referred to for the specific steps performed by the digital financial service platform 100 and the information presentation device 200.

In order to solve the technical problem in the foregoing background, fig. 2 is a schematic flowchart of an information parsing method based on cloud computing and big data according to an embodiment of the present application, where the information parsing method based on cloud computing and big data according to the present embodiment may be executed by the digital financial service platform 100 shown in fig. 1, and the information parsing method based on cloud computing and big data is described in detail below.

Step S110, obtaining an interest portrait between the target session service object and the corresponding associated session service object, and sending push information to the information display device 200 corresponding to the target session service object and the associated session service object based on the interest portrait.

In this embodiment, the interest portrayal between the target session service object and the corresponding associated session service object is obtained based on a big data session node sequence between the target session service object and the associated session service object in the target session process page, which will be described in detail later.

Step S120, target interaction operation data of the target session service object and the associated session service object for the push information is obtained, and a target tracking data stream obtained by tracking the interaction object in the target interaction service is obtained according to the target interaction operation data.

In an alternative embodiment, after the push information is pushed to the information display device 200 corresponding to the target session service object and the associated session service object, the target session service object and the associated session service object may perform an interaction based on the push information, there may be an interaction operation process in the interaction process, and the interaction operation processes may be completed in the digital financial service platform 100 by interaction, so that the digital financial service platform 100 may directly obtain target interaction operation data of the target session service object and the associated session service object for the push information at this time, and obtain a target tracking data stream obtained by tracking the interaction object in the target interaction service according to the target interaction operation data.

The interactive object may include an interactive initiation object and an interactive element corresponding to the interactive initiation object. The interaction initiating object may refer to a scene object that triggers interaction in the interaction triggering process, and the interaction element may refer to an interaction control, such as a barrage comment interaction control, when the scene object performs interaction.

Step S130, determining a first trace data segment and a second trace data segment to be processed from the target trace data stream, and obtaining a first data association degree between the first trace data segment and the second trace data segment. Wherein the first trace data segment precedes the second trace data segment in the target trace data stream by the trace node that appears in the first trace data segment.

For example, the first trace data segment may be a trace data segment in which a trace node appears in the target trace data stream for a first time length before the second trace data segment, and for example, when the second trace data segment is processed to determine whether it needs to be sent to the server, the first trace data segment may be determined according to the trace data segment corresponding to the first time length of the second trace data segment before the target time in the target trace data stream. The first duration is set to facilitate the determination of the state of the interactive element corresponding to the interactive initiation object presented by the second trace data segment according to the degree of association between the first trace data segment and the second trace data segment. Here, the first duration may be determined according to the type of the object in the target interactive service, for example, for a conventional interactive process, the first duration may be selected to be about 1 second.

Step S140, determining the state of the interactive element corresponding to the interactive initiation object presented by the second tracking data segment according to the first data association degree, and sending the second tracking data segment to the cloud computing container for data mining under the condition that the state of the interactive element corresponding to the interactive initiation object indicates that the changed target interactive element exists.

The cloud computing container can be used for data mining of a process of the interactive element changing at the interactive initiation object, and can run on a cloud computing platform. The specific process of data mining is not an inventive point of the embodiment of the present application, and may refer to a conventional data mining strategy in the prior art, which is not described herein again.

Based on the above steps, in this embodiment, the first data association degree between the first trace data segment and the second trace data segment in the target trace data stream is adopted to determine the state of the interactive element corresponding to the interaction initiating object, which is presented by the second trace data segment, and in the case that the state of the interactive element corresponding to the interaction initiating object indicates that there is a changed target interactive element, the number of the trace data segments which need to be sent is reduced by sending only the second trace data segment indicating that there is a changed target interactive element, and the sent trace data segment has a changed target interactive element, which is more convenient for data mining, so that the load pressure of data communication is reduced, and the calculation amount of data mining is reduced.

In one possible implementation manner, for step S130, in the process of obtaining the first data association degree between the first trace data segment and the second trace data segment, the following exemplary sub-steps may be implemented.

And a substep S131, comparing the data characteristic segment of the first trace data segment with the data characteristic segment of the second trace data segment to obtain the association degree of the first trace data segment.

In this embodiment, the association degree of the first trace data segment may refer to a ratio of similar data segments (or data regions) existing in the data feature segment of the first trace data segment and the data feature segment of the second trace data segment to all data segments (or data regions), that is, a similarity.

In the substep S132, a loss degree (e.g., difference degree) between the contrast association degree and the association degree of the first trace data segment is obtained as the first data association degree.

The comparison association degree may be a second trace data segment association degree between a third trace data segment and a fourth trace data segment in the target trace data stream, where the third trace data segment and the fourth trace data segment are trace data segments in a target trace node segment where the target trace data stream starts to record. In this way, in order to accurately determine the state of the subsequent interactive element in different scenes, in the embodiment, the state of the interactive element can be determined more accurately by comparing the data association degree determined by the loss degree of the association degree with the first trace data segment.

In a possible implementation manner, for step S140, in the process of determining the state of the interaction element corresponding to the interaction initiating object presented by the second trace data segment according to the first data association degree, the following exemplary sub-steps may be implemented.

In the substep S141, an interactive mark corresponding to the target tracking data stream is obtained.

Wherein, interactive token can include: the interactive system comprises a first interactive mark used for indicating that an interactive element corresponding to an interactive initiating object is positioned at a first interactive process node and a second interactive mark used for indicating that an interactive element corresponding to the interactive initiating object is positioned at a second interactive process node, wherein the switching quantity of the interactive element corresponding to the interactive initiating object under the second interactive process node is greater than that of the interactive element corresponding to the interactive initiating object under the first interactive process node.

It should be noted that the first interactive mark may indicate that the interactive initiating object is performing interaction of the interactive elements with a change of an unobvious switching amount, and the interactive elements are in a state of small variation intensity, and the second interactive mark may indicate that the interactive initiating object is performing interaction of the interactive elements with a change of an obvious switching amount, and the interactive elements are in a state of large variation intensity.

In the sub-step S142, when the first data association degree is less than or equal to the first association degree and the interaction mark is the first interaction mark, it is determined that the state of the interaction element corresponding to the interaction initiation object is the switching state.

Wherein, the switching state can be used to indicate that the target interaction element exists in the interaction process of the interaction initiating object.

And a substep S143, determining that the state of the interaction element corresponding to the interaction initiating object is a switching state when the association degree of the first data is greater than or equal to the second association degree and the interaction mark is the second interaction mark.

In a possible implementation manner, before obtaining the interaction token corresponding to the target tracking data stream, this embodiment may further detect whether the target tracking data stream is configured with the interaction token, and determine a second data association degree between a fifth tracking data segment and a sixth tracking data segment in the target tracking data stream when detecting that the target tracking data stream is not configured with the interaction token, where a tracking node of the fifth tracking data segment in the target tracking data stream is prior to the sixth tracking data segment.

In this way, under the condition that the second data association degree is greater than the first association degree and less than or equal to the second association degree, generating a first interaction mark; and generating a second interactive mark under the condition that the second data association degree is greater than the second association degree.

And under the condition that the interactive mark is a first interactive mark and the first data association degree is smaller than the first association degree, the second tracking data segment is sent to the cloud computing container and then the first interactive mark is deleted. And under the condition that the interactive mark is a second interactive mark and the first data association degree is greater than the first association degree and less than the second association degree, converting the second interactive mark into the first interactive mark. And deleting the second interactive mark under the condition that the interactive mark is the second interactive mark and the first data association degree is smaller than the first association degree.

Here, when the interactive mark is the first interactive mark and the first data association degree is smaller than the first association degree, since the second trace data segment after the state switching is already sent to the cloud computing container, it is determined again that the data association degree is smaller than the first association degree subsequently, it indicates that the current trace data segment is not changed, and the first interactive mark is deleted after the second trace data segment is sent. When the change interactive mark is the second interactive mark and the first data association degree is between the first association degree and the second association degree, the change interactive mark indicates that the current switching data volume is reduced, the tracking data segment does not need to be uploaded completely, and the second interactive mark is converted into the first interactive mark, so that the uploading number of the tracking data segment is reduced.

In a possible implementation manner, still referring to step S140, in the process of sending the second trace data segment to the cloud computing container in the case that the state of the interactive element corresponding to the interaction initiating object indicates that there is a changed target interactive element, the following exemplary sub-steps may be implemented.

And a substep S144, adding the second trace data segment to the trace data segment uploading queue under the condition that the state of the interactive element corresponding to the interactive initiating object indicates that there is a changed target interactive element.

And a substep S145, sequentially transmitting the tracking data segments in the tracking data segment uploading queue to the cloud computing container in sequence under the condition that the number of the tracking data segments in the tracking data segment uploading queue is less than the first number.

And a substep S146, aggregating the previous x tracking data segments in the tracking data segment uploading queue into a tracking data segment data packet and sending the tracking data segment data packet to the cloud computing container under the condition that the number of the tracking data segments in the tracking data segment uploading queue is greater than or equal to the first number.

And the trace data segments in the trace data segment uploading queue are arranged according to the sequence of the trace nodes in the target trace data stream.

Further, in a possible implementation manner, still referring to step S130, in the process of determining the first trace data segment and the second trace data segment to be processed from the target trace data stream, the following exemplary sub-steps may be implemented.

And a substep S133 of selecting a first trace data segment and a second trace data segment from the target trace data stream, wherein the first trace data segment precedes the second trace data segment in the trace nodes appearing in the target trace data stream.

The substep S134 determines the first trace data segment as the first trace data segment.

And a substep S135 of determining the second trace data segment as the second trace data segment.

Alternatively, it may also be realized by the following exemplary substeps.

In the sub-step S136, a first trace data segment and a second trace data segment are selected from the target trace data stream, and a trace node of the first trace data segment appearing in the target trace data stream precedes the second trace data segment.

And a substep S137, performing segmentation processing on the first trace data segment to obtain a first segmented trace data segment sequence, and performing segmentation processing on the second trace data segment to obtain a second segmented trace data segment sequence.

In the substep S138, the w-th sliced trace data segment in the first sliced trace data segment sequence is used as the first trace data segment, and the w-th sliced trace data segment in the second sliced trace data segment sequence is used as the second trace data segment.

It should be noted that, the first trace data segment and the second trace data segment are the cut trace data segments obtained by cutting the trace data segment frame, and it can be understood that, because the state of the interaction element corresponding to the interaction initiating object needs to be determined according to the first state similarity between the first trace data segment and the second trace data segment, the first trace data segment and the second trace data segment should be cut trace data segments in the same trace data area. It can be understood that, when the slicing processing is performed, the sliced trace data segments after being sliced may be arranged in sequence to obtain a trace data segment sequence, and then whether the first trace data segment and the second trace data segment represent the same trace data region may be determined according to a position in the sequence. Of course, the segmented trace data segment after the segmentation processing may also be labeled according to the trace data region, the segmented trace data segment at the first trace data region in the first frame of trace data segment is determined to be the first trace data segment, and the segmented trace data segment at the first trace data region in the second frame of trace data segment is determined to be the second trace data segment, where the label of the trace data region of the first trace data segment is the same as that of the second trace data segment.

In a further possible implementation manner, for step S110, in a process of obtaining an interest representation between a target session service object and a corresponding associated session service object, and sending push information to the information presentation device 200 corresponding to the target session service object and the associated session service object based on the interest representation, the following exemplary sub-steps may be implemented.

And a substep S111, acquiring a big data session node sequence between the target session service object and the associated session service object in the target session process page.

In this embodiment, the big data session node sequence may specifically include a plurality of target session nodes invoked by the target session service object in the target session process page in the target session subscription service, a plurality of associated session nodes invoked by the associated session service object in the target session process page in the target session subscription service, and an invocation service location of each session node.

It should be noted that the target session process page may be a process page in the application program for the user (e.g., the target session service object) to perform session interaction with another user (e.g., the associated session service object). The target session subscription service may refer to a service (e.g., live e-commerce service, online consultation service, etc.) to which a user (e.g., a target session service object) subscribes in advance with other users (e.g., associated session service objects). The session node may refer to a service data recording process when session interaction is performed each time, and may be a session node in which a tracking node is used as a unit, or a session node in which a certain data area is used as a unit, which is not specifically limited herein. In addition, invoking the service location may refer to a service function module of a service that is specifically invoked each time a session interaction is performed, such as a viewing service function module of an e-commerce voice interaction of an e-commerce live broadcast service.

And a substep S112, constructing a session node intention list by using the target session node intention lists corresponding to the target session nodes and the associated session node intention lists corresponding to the associated session nodes, and acquiring session node intention description information according to the session node intention list.

In this embodiment, the target session node intention list is used to represent key intention label distribution of a plurality of target session nodes interacting according to a call service location, the associated session node intention list is used to represent key intention label distribution of a plurality of associated session nodes interacting according to a call service location, and the session node intention description information is used to represent intention interest degrees of the target session node intention list and the associated session node intention list.

In this embodiment, the big data session node sequence may include, but is not limited to, a session node sequence under a preset scenario, optionally, the preset scenario may be, but is not limited to, an interaction scenario, which may include, but is not limited to, an e-commerce interaction scenario, an information interaction scenario, and the like, optionally, the session node sequence may include, but is not limited to, a session node associated with the preset scenario, the session node sequence may include, but is not limited to, a session node related to a preset rule, the preset rule may include, but is not limited to, an endorse, a message content, and the like, the session node sequence may include, but is not limited to, an endorse session node, a forward session node, a comment session node, a message passing session node, and the like, optionally, the key intention tag distribution of the session node may be, but is not limited to, represent session nodes under the same type, for example, session nodes under the forward session node tag, regardless of forwarding action, may belong to a key intent tag distribution of forwarding session nodes.

It is worth noting, among other things, that key intent tags may refer to category tags for specific intents. This can be done by intention recognition. The intention identification is to classify the target session node into corresponding intention categories by means of classification. For example, if the user wants to listen to a song, the intention of the target session node is a music intention of type XX, and the intention of the user wants to listen to a phase sound of type B is a station intention. After intent recognition is completed, it can be applied to multiple domains, for example, in the field of search engines using intent recognition to obtain information most relevant to user-initiated session nodes. For example, when a user queries a certain search keyword, if the search keyword includes a game, a movie, a song, and the like, and the user is found to want to play the game through intention identification, the query result of the game is directly returned to the user, so that the number of search clicks of the user is saved, the search tracking node is shortened, and the user experience is greatly improved.

For another example, when the method is applied to a chat robot, assuming that a chat robot has only 30 skills at present, a user sends an instruction to the chat robot, and the chat robot firstly identifies session nodes of the user to one or more information push nodes according to intention, and then performs subsequent processing. After the intention recognition is completed, the robot can accurately understand the intention of the user and then accurately give a reply for each session node sent by the user to the robot.

In this embodiment, the intention interest level may refer to an interest proximity level where the same matching intention exists between the target session node intention list and the associated session node intention list. For example, the target session node intention list of the W1 user includes session node intents for the Q1 service, the target session node intention list of the W2 user includes session node intents for the Q2 service, and the Q1 service and the Q2 service belong to associated sub-services under the Q service, indicating that the interests are closer, and the interest degree of the intents is correspondingly higher.

And a substep S113, constructing a session node interest sequence by using the target session node and the associated session node which are called in the target session subscription service segment and are in the big data session node sequence and according to the service flow direction of the calling service position, and acquiring session node interest description information according to the session node interest sequence.

In this embodiment, the session node interest description information is used to characterize an intention interest level between at least two mapped session nodes in the session node interest sequence. The service flow may refer to a switching process of a service specifically called in a session interaction initiating process. A mapping session node may refer to a set of session nodes that a target session node forms with associated session nodes that are mapped.

And a substep S114, obtaining the session interest degree between the target session service object and the associated session service object according to the session node intention description information and the session node interest description information, determining an interest portrait between the target session service object and the associated session service object according to the session interest degree, and sending push information to the information display device 200 corresponding to the target session service object and the associated session service object based on the interest portrait.

Optionally, the interest portrait obtaining strategy can be applied to, but not limited to, mining scenes of interest portraits, and can also be applied to, but not limited to, recommendation and precise marketing based on information push services.

The interest portrayal can abstract an information overview of conversation interaction between the target conversation service object and the associated conversation service object, and provides a data basis for further accurately and quickly analyzing important information such as user behavior habits, consumption habits and the like. For example, in the present embodiment, the interest portraits may include, but are not limited to, lover interest portraits, colleague interest portraits, and the like.

Alternatively, the session interestingness may be, but is not limited to, a positive or negative correlation between the confidence that the indicated interest representation is an interest representation between the target session service object and the associated session service object.

Optionally, the method for obtaining the interest representation may be implemented by, but not limited to, a target session service object and a session node of an associated session service object in a target session process page, in other words, the target session service object and the associated session service object in this embodiment are only for illustration, and the number of session service objects or the number of session nodes is not limited.

In this embodiment, in the process of determining the interest representation between the target session service object and the associated session service object according to the session interest, the target interest representation with the largest session interest may be selected to be determined as the interest representation between the target session service object and the associated session service object, or the target interest representation with the session interest greater than a preset interest threshold may be selected to be determined as the interest representation between the target session service object and the associated session service object, or the target interest representations with the session interest ranked from large to small by N (N is a positive integer) may also be selected to be determined as the interest representation between the target session service object and the associated session service object, which is not limited specifically.

Based on the above steps, according to the embodiments provided by the present application, after constructing a session node intention list by using a target session node intention list corresponding to a plurality of target session nodes and an associated session node intention list corresponding to a plurality of associated session nodes, and obtaining session node intention description information according to the session node intention list, constructing a session node interest sequence by using a target session node and an associated session node which are called in a target session subscription service section and in a service flow direction of a calling service location in a big data session node sequence, and obtaining session node interest description information according to the session node interest sequence, and obtaining a session interest level between the target session service object and the associated session service object according to the session node intention description information and the session node interest description information, thereby determining an interest profile between the target session service object and the associated session service object, and sending push information to the information display equipment 200 corresponding to the target session service object and the associated session service object, and inputting the information through the session nodes for representing the intention interest degrees of various session node intentions so as to achieve the aim of acquiring more accurate interest portraits, thereby improving the acquisition accuracy of the interest portraits and improving the service matching precision of information push.

In one possible implementation manner, for step S112, in the process of constructing the session node intention list by using the target session node intention lists corresponding to the plurality of target session nodes and the associated session node intention lists corresponding to the plurality of associated session nodes, the target session node list and the associated session node list may be compared to obtain a plurality of preference intents.

Wherein the preference intention includes at least one session node intention satisfying the minimum intention determination condition, the method for specifically performing intention recognition may be a rule method based on a dictionary and a template, for example, different domain dictionaries that different intentions may have, such as a book name, a song name, a product name, and the like. When a user's intention appears, a judgment can be made based on the degree of matching or coincidence between the intention and the dictionary. For another example, the user's intention may be obtained by clicking the log if the service scenario is a type of service scenario such as a search engine based on the query click log. For another example, the intention of the user may also be determined based on a classification model, since the intention identification itself is also a classification problem, the implementation method actually adopted is the same as the conventional classification model method, and details are not repeated here.

Then, according to the plurality of preference intents, a plurality of session node intention lists are obtained.

Wherein the plurality of session node intent lists are used to construct the session node intent list.

In more detail, based on the above description, in a possible implementation manner, for step S112, in the process of constructing the session node intention list by using the target session node intention lists corresponding to the plurality of target session nodes and the associated session node intention lists corresponding to the plurality of associated session nodes, the following exemplary sub-steps can be implemented, which are described in detail as follows.

And a substep S1121, taking the target session node list as a current session node list, and repeatedly executing the following steps until the associated session node list is traversed.

In sub-step S1122, the current session node intention is determined from the current session node list.

And a substep S1123 of comparing the current session node intention with each session node intention in the associated session node list in turn.

In sub-step S1124, in the case where there is a session node intention identical to the current session node intention in the associated session node list, the current session node intention is taken as a preference intention.

In the sub-step S1125, when there is no session node intention identical to the current session node intention in the associated session node list, the next session node intention is obtained from the current session node list as the current session node intention.

In one possible implementation manner, for step S113, in the process of constructing a session node interest sequence by using the target session node and the associated session node that are called in the target session subscription service segment in the big data session node sequence and flow according to the service flow of the calling service location, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1131, performing call service positioning on the target session node and the associated session node in the target session subscription service segment in the big data session node sequence to obtain a plurality of call service positioning information.

And a substep S1132, performing service flow direction indexing on the multiple pieces of calling service positioning information according to the calling service positions, and recording a session content relationship between a target session node and an associated session node corresponding to the matched service flow direction relationship according to a service flow direction indexing result to construct a session node interest sequence.

In a possible implementation manner, still referring to step S112, in the process of obtaining the session node intention description information according to the session node intention list, the session node intention list may be input into the first artificial intelligence module, and the session node intention description information output by the first artificial intelligence module may be obtained. The first artificial intelligence module is used for capturing association characteristics among all session node intention elements in the session node intention list.

In addition, in a possible implementation manner, still referring to step S113, in the process of obtaining the session node interest description information according to the session node interest sequence, the session node interest sequence may be input into the first artificial intelligence module, and then the session node interest description information output by the first artificial intelligence module is obtained.

In a possible implementation manner, further to step S114, in the process of obtaining the session interest level between the target session service object and the associated session service object according to the session node intention description information and the session node interest description information, the following exemplary sub-steps may be implemented.

And a substep S1141 of inputting target session node input information into the second artificial intelligence module, wherein the target session node input information is used for representing session node intention description information and session node interest description information.

And a substep S1142 of obtaining an output result of the second artificial intelligence module, wherein the output result is used for representing the conversation interest degree.

Illustratively, as an alternative example only, the second artificial intelligence module described above may be configured by the following embodiments, which are described in detail below.

(1) And acquiring a plurality of calibration session node sequences.

The calibration session node sequence at least comprises a plurality of first calibration session nodes and a plurality of associated calibration session nodes which are respectively called by a target calibration session service object and an associated calibration session service object which are both the interest portrait in a target calibration session process page, and calibration calling service positions of all the calibration session nodes.

(2) And sequentially taking each calibration session node sequence as the current calibration session node sequence to execute the following operations until the termination requirement is met.

(3) And constructing a calibration session node intention list by utilizing the first calibration session node intention lists corresponding to the plurality of first calibration session nodes and the associated calibration session node intention lists corresponding to the plurality of associated calibration session nodes, and acquiring the calibration session node intention description information according to the calibration session node intention list.

(4) And constructing a calibrated session node interest sequence by utilizing a first calibrated session node which is called in a target session subscription service section in the current calibrated session node sequence and is based on the service flow direction of the calibrated calling service position and a related calibrated session node, and acquiring calibrated session node interest description information according to the calibrated session node interest sequence.

(5) And inputting the input information of the target calibration session node into the current second artificial intelligence module.

The target calibration session node input information is calibration session node intention description information and calibration session node interest description information.

(6) And acquiring the current output result of the second artificial intelligence module.

The current output result comprises a third confidence degree that the target calibration session service object and the associated calibration session service object are third interest images, and a fourth confidence degree that the target calibration session service object and the associated calibration session service object are other interest images.

(7) And under the condition that the current output result meets the termination requirement, determining the current second artificial intelligence module as a trained second artificial intelligence module.

In a possible implementation manner, further to step S114, in the process of sending push information to the information presentation device 200 corresponding to the target session service object and the associated session service object based on the interest representation, the following exemplary sub-steps may be implemented.

And a substep S1143 of obtaining a portrait label push page of the interest portrait and corresponding to each index push page to be associated.

And a substep S1144 of respectively extracting the page entries of the portrait label pushed page and each to-be-associated index pushed page to obtain a portrait label pushed page entry sequence and each to-be-associated index pushed page entry sequence, and calculating the subject association degree of the portrait label pushed page entry sequence and each to-be-associated index pushed page entry sequence to obtain each subject matching feature.

And a substep S1145 of matching interface services based on the portrait label push page and each index push page to be associated to obtain matching characteristics of each interface service.

And a substep S1146 of calculating the sketch label pushing page and each page knowledge map corresponding to each to-be-associated index pushing page, and calculating to obtain each page knowledge distribution characteristic based on the page knowledge maps.

And a substep S1147 of calculating the correlation degree of the portrait label push page and each index push page to be correlated respectively based on the interface service matching feature, the theme matching feature and the page knowledge distribution feature.

And a substep S1148 of obtaining a target index push page of each to-be-associated index push page according to the correlation degree between the portrait label push page and each to-be-associated index push page.

In the sub-step S1149, the portrait label push page and the target index push page are sent to the information display device 200 corresponding to the target session service object and the associated session service object as push information.

For example, in one possible implementation manner, for the sub-step S1144, in the process of respectively performing page entry extraction on the portrait tag pushed page and each to-be-associated index pushed page to obtain a portrait tag pushed page entry sequence and each to-be-associated index pushed page entry sequence, the following exemplary implementation manner may be implemented.

(1) And inputting the portrait label pushed page into a page entry extraction model to extract a page entry, so as to obtain a portrait label pushed page entry sequence.

(2) And respectively inputting each to-be-associated index pushed page into a page entry extraction model for page entry extraction to obtain each to-be-associated index pushed page entry sequence. The page entry extraction model is obtained by training a push page by using a neural network algorithm according to entry extraction calibration.

In a possible implementation manner, for the sub-step S1145, in the process of performing interface service matching on the portrait tag pushed page and each to-be-associated index pushed page to obtain each interface service matching feature, the following exemplary embodiments may be implemented.

(1) Determining a current to-be-associated index push page from each to-be-associated index push page, performing page entry extraction on the portrait tag push page to obtain a portrait tag push page entry sequence, and performing page entry extraction on the current to-be-associated index push page to obtain a current to-be-associated index push page entry sequence.

(2) And performing entry feature extraction on the page entry sequence pushed based on the portrait label and the current index to be associated to push the page entry sequence to obtain entry feature extraction information.

(3) Fusing the portrait tag pushed page entry sequence, the current to-be-associated index pushed page entry sequence and entry feature extraction information to obtain target fusion features, and performing interface service matching based on the target fusion features to obtain the interface service matching degree of the portrait tag pushed page and the current to-be-associated index pushed page.

(5) And taking the interface service matching degree as the interface service matching characteristic corresponding to the portrait label push page and the current to-be-associated index push page. The interface service matching model is obtained by calibrating a push page according to interface service matching and using a page entry extraction model to carry out interface service matching training.

The generation process of the interface service matching model may be: firstly, a page entry extraction model is obtained, and an initial interface service matching model is obtained according to the page entry extraction model. And then, acquiring an interface service matching calibration push page, wherein the interface service matching calibration push page comprises a structured push page and an unstructured push page, inputting the structured push page and the unstructured push page into an initial interface service matching model for training, and acquiring the interface service matching model when the training is finished.

The generation process of the page entry extraction model may be: firstly, obtaining a vocabulary entry extraction calibration push page, splitting the vocabulary entry extraction calibration push page to obtain a page splitting result, and obtaining the characteristic information of a page splitting unit corresponding to the page splitting result. And then, inputting the characteristic information of the page splitting unit into the initial deep learning network and the trained page vocabulary entry extraction model for forward calculation to obtain a first calculation information sequence and a second calculation information sequence which are output. On the basis, calculating the topic association degree of the first calculation information sequence and the second calculation information sequence, calculating the sum of the topic association degree and a preset first matching contrast to obtain a target sum value, calculating the ratio of the target sum value to a preset second matching contrast, and comparing the preset first matching contrast with the ratio to obtain a preset function value. And when the preset function value does not meet the preset condition, performing back propagation updating on the initial deep learning network according to the preset function value to obtain the deep learning network for updating the model parameters.

On the basis, the deep learning network with updated model parameters can be used as an initial deep learning network, the steps of inputting the characteristic information of the page splitting unit into the initial deep learning network and the trained page entry extraction model for forward calculation to obtain a first calculation information sequence and a second calculation information sequence which are output are returned, and the trained deep learning network is used as the page entry extraction model until the preset function value obtained by training meets the preset condition.

For example, in one possible implementation manner, for the sub-step S1146, in the process of calculating each page knowledge graph corresponding to the portrait tab pushed page and each to-be-associated index pushed page, and obtaining each page knowledge distribution feature based on each page knowledge graph, the following exemplary implementation manner may be implemented.

(1) Determining a current index push page to be associated from each index push page to be associated, and respectively splitting page attributes of the portrait label push page and the current index push page to be associated to obtain each index object and each current index page attribute to be associated.

(2) Inputting each index object into a word page vocabulary entry extraction model for carrying out page vocabulary entry extraction to obtain index object distribution, and inputting each current index page attribute to be associated into the word page vocabulary entry extraction model for carrying out page vocabulary entry extraction to obtain current index page attribute distribution to be associated.

(3) And calculating the association attribute relation between the index object distribution and the attribute distribution of the current to-be-associated index page to obtain a target page knowledge map corresponding to the portrait label push page and the current to-be-associated index push page.

(4) And calculating the structural relationship characteristics and the element relationship characteristics corresponding to the target page knowledge graph, and calculating the knowledge resource characteristics corresponding to the target page knowledge graph.

For example, the structure drawing feature parameters corresponding to each structure drawing relationship may be obtained from the target page knowledge graph, and the fusion feature parameters of each structure drawing feature parameter may be calculated to obtain the structure relationship features.

And obtaining element drawing characteristic parameters corresponding to each matrix column from the target page knowledge graph, and calculating fusion characteristic parameters of the element drawing characteristic parameters to obtain element relation characteristics.

And extracting each knowledge resource characteristic parameter of each knowledge resource node in the target page knowledge graph, and calculating the fusion characteristic parameter of each knowledge resource characteristic parameter to obtain the knowledge resource characteristics.

(5) And fusing the structural relationship characteristic, the element relationship characteristic and the knowledge resource characteristic to obtain a current page knowledge distribution characteristic corresponding to the portrait label push page and the current to-be-associated index push page.

For example, in one possible implementation manner, for sub-step S1147, in the process of calculating the correlation degree between the portrait tag pushed page and each to-be-associated index pushed page based on the interface service matching feature, the topic matching feature and the page knowledge distribution feature, the portrait tag pushed page feature may be extracted from the portrait tag pushed page, and the to-be-associated index pushed page feature may be extracted from each to-be-associated index pushed page. And then, calculating the correlation degree of the portrait label push page and each index push page to be correlated respectively based on the portrait label push page feature, the index push page feature to be correlated, the interface service matching feature, the theme matching feature and the page knowledge distribution feature.

For example, in another possible implementation manner, for the sub-step S1147, in the process of calculating the degree of correlation between the portrait tag pushed page and each to-be-associated index pushed page based on the interface service matching feature, the topic matching feature, and the page knowledge distribution feature, the interface service matching feature, the topic matching feature, and the page knowledge distribution feature may be fused to obtain a fused feature, and then the fused feature is input into the page index association model to be calculated to obtain the degree of correlation between the portrait tag pushed page and the to-be-associated index pushed page.

The page index association model is obtained by training feature data formed by the interface service matching features, the theme matching features and the page knowledge distribution features by using a regression decision tree.

Fig. 3 is a schematic functional module diagram of an information analysis device 300 based on cloud computing and big data according to an embodiment of the present disclosure, in this embodiment, functional modules of the information analysis device 300 based on cloud computing and big data may be divided according to the method embodiment executed by the digital financial service platform 100, that is, the following functional modules corresponding to the information analysis device 300 based on cloud computing and big data may be used to execute each method embodiment executed by the digital financial service platform 100. The cloud computing and big data based information parsing apparatus 300 may include a first obtaining module 310, a second obtaining module 320, a determining module 330, and a sending module 340, and the functions of the functional modules of the cloud computing and big data based information parsing apparatus 300 are described in detail below.

The first obtaining module 310 is configured to obtain an interest representation between a target session service object and a corresponding associated session service object, and send push information to the information display apparatus 200 corresponding to the target session service object and the associated session service object based on the interest representation, where the interest representation between the target session service object and the corresponding associated session service object is obtained based on a big data session node sequence between the target session service object and the associated session service object in a target session progress page. The first obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the first obtaining module 310, reference may be made to the detailed description of the step S110.

The second obtaining module 320 is configured to obtain target interaction operation data of the target session service object and the associated session service object for the push information, and obtain a target tracking data stream obtained by tracking an interaction object in the target interaction service according to the target interaction operation data, where the interaction object in the target interaction service includes an interaction initiating object and an interaction element corresponding to the interaction initiating object. The second obtaining module 320 may be configured to perform the step S120, and for a detailed implementation of the second obtaining module 320, reference may be made to the detailed description of the step S120.

The determining module 330 is configured to determine a first trace data segment and a second trace data segment to be processed from the target trace data stream, and acquire a first data association degree between the first trace data segment and the second trace data segment, where a trace node of the first trace data segment appearing in the target trace data stream is prior to the second trace data segment. The determining module 330 may be configured to perform the step S130, and the detailed implementation of the determining module 330 may refer to the detailed description of the step S130.

The sending module 340 is configured to determine, according to the first data association degree, a state of an interaction element corresponding to the interaction initiating object, which is present in the second tracking data segment, and send the second tracking data segment to the cloud computing container for data mining under the condition that the state of the interaction element corresponding to the interaction initiating object indicates that there is a changed target interaction element, where the cloud computing container is used for performing data mining on a process in which the interaction element changes at the interaction initiating object. The sending module 340 may be configured to execute the step S140, and the detailed implementation manner of the sending 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 obtaining 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 obtaining 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 the digital financial service platform 100 for implementing the cloud computing and big data based information parsing method according to the embodiment of the present disclosure, where the digital financial service platform 100 may be implemented on a cloud server. As shown in fig. 4, the digital financial services platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the first obtaining module 310, the second obtaining module 320, the determining module 330, and the sending module 340 included in the cloud computing and big data based information parsing apparatus 300 shown in fig. 3), so that the processor 110 may execute the cloud computing and big data based information parsing 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 information presentation device 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the digital financial services platform 100, which implement similar principles and technical effects, and this embodiment is not described herein again.

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, the embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the information analysis method based on cloud computing and big data is realized.

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 represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

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

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a passive programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, as a stand-alone sequence on the user's computer, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences are processed, the use of alphanumeric characters, or the use of other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless explicitly stated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

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

Claims

1. An information analysis method based on cloud computing and big data is applied to a digital financial service platform, the digital financial service platform is in communication connection with a plurality of information display devices, and the method comprises the following steps:

determining the state of the interaction element corresponding to the interaction initiating object, which is presented by the second tracking data segment, according to the first data association degree, and sending the second tracking data segment to a cloud computing container for data mining under the condition that the state of the interaction element corresponding to the interaction initiating object indicates that a changed target interaction element exists, wherein the cloud computing container is used for performing data mining on the process that the interaction element changes at the interaction initiating object;

the step of determining the state of the interaction element corresponding to the interaction initiation object presented by the second tracking data segment according to the first data association degree includes:

2. The cloud computing and big data based information parsing method according to claim 1, wherein the step of obtaining the first data association degree between the first trace data segment and the second trace data segment includes:

3. The cloud computing and big data based information parsing method of claim 1, wherein prior to the obtaining of the interactive token corresponding to the target tracking data stream, the method further comprises:

4. The information analysis method based on cloud computing and big data according to claim 1, wherein the step of sending the second trace data segment to a cloud computing container when the state of the interactive element corresponding to the interaction initiating object indicates that there is a target interactive element that changes includes:

5. The information analysis method based on cloud computing and big data according to any one of claims 1 to 4, wherein the step of determining a first trace data segment and a second trace data segment to be processed from the target trace data stream includes:

determining the first trace data segment as the first trace data segment;

6. The information analysis method based on cloud computing and big data according to any one of claims 1 to 4, wherein the step of obtaining an interest portrait between a target session service object and a corresponding associated session service object, and sending push information to an information presentation device corresponding to the target session service object and the associated session service object based on the interest portrait comprises:

7. The method according to claim 6, wherein the step of constructing a session node interest sequence using the target session node and the associated session node that are called in a target session subscription service segment and flow according to the service of the calling service location in the big data session node sequence comprises:

8. The method according to claim 6, wherein the step of sending push information to the information presentation devices corresponding to the target session service object and the associated session service object based on the interest representation comprises:

and sending the portrait label push page and the target index push page as the push information to the information display equipment corresponding to the target session service object and the associated session service object.

9. A digital financial services platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to be communicatively connected to at least one information presentation device, the machine-readable storage medium is configured to store a program, instructions, or codes, and the processor is configured to execute the program, instructions, or codes in the machine-readable storage medium to perform the cloud computing and big data based information parsing method according to any one of claims 1 to 8.