阅读说明：本技术 基于时序特征的业务信息处理方法及服务器 (Service information processing method based on time sequence characteristics and server ) 是由 吴潇琦 于 2021-11-12 设计创作，主要内容包括：本发明是关于基于时序特征的业务信息处理方法及服务器,在应用本申请实施例时,可以对抽取的意图描述和兴趣描述进行关联以获得业务日志内容和潜在偏好的关联描述,该关联描述涵盖更加完整多样的日志数据,此外,该关联描述能够应对会话场景更新的干扰,减少行为意图挖掘的误差,进而再对关联描述启用更新操作以获得具有更高特征识别度的数字化交互业务日志。本申请实施例对数字化交互业务日志进行特征识别度校正操作,能够提高在线交互业务日志的特征识别度,这样能够确保在线交互业务日志能够适用于尽可能多的需求分析任务。(The invention relates to a service information processing method and a server based on time sequence characteristics, when the embodiment of the application is applied, extracted intention description and interest description can be associated to obtain association description of service log content and potential preference, the association description covers more complete and diverse log data, in addition, the association description can deal with the interference of session scene updating, the error of behavior intention mining is reduced, and then the association description is enabled to be updated to obtain a digital interactive service log with higher characteristic identification degree. The embodiment of the application carries out the characteristic identification degree correction operation on the digital interactive service log, can improve the characteristic identification degree of the online interactive service log, and thus can ensure that the online interactive service log can be suitable for as many demand analysis tasks as possible.)

1. A service information processing method based on time sequence characteristics is applied to a service information processing server, and the method at least comprises the following steps:

determining a digital interactive service log, wherein the digital interactive service log comprises a first online interactive service log and a second online interactive service log which point to the same service session event with time sequence characteristics and are crawled in the same service session service state;

extracting a first interaction behavior intention distribution of the digital interaction service log, a first potential interest description distribution of the digital interaction service log, and a second interaction behavior intention distribution associating service session description content and potential interest content of the digital interaction service log;

performing key description association operation on the digital interaction service log, a first interaction behavior intention distribution, a first potential interest description distribution and a second interaction behavior intention distribution of the digital interaction service log to obtain an associated interaction behavior intention distribution of the digital interaction service log; and starting an updating operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the characteristic identification degree correction operation.

2. The method of claim 1, wherein the enabling an update operation on the associated interaction behavior intent distribution of the digitized interaction service log to obtain the digitized interaction service log after a feature recognition correction operation comprises: and respectively starting feature mining operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the feature recognition degree correction operation.

3. The method of claim 2, wherein the extracting the first interaction behavior intent distribution of the digitized interaction traffic log comprises:

respectively starting first feature mining operation aiming at the first online interactive service log and the second online interactive service log to obtain first transition interactive behavior intention distribution corresponding to the first online interactive service log and the second online interactive service log in sequence;

respectively starting second characteristic mining operation aiming at the first transition interaction behavior intention distribution of the first online interaction service log and the second online interaction service log to obtain second transition interaction behavior intention distribution of a dynamic layer corresponding to the first online interaction service log and the second online interaction service log in sequence;

and respectively starting loss detection operation aiming at the second transition interaction behavior intention distribution of each layer of the first online interaction service log and the second online interaction service log to obtain first interaction behavior intention distribution corresponding to the first online interaction service log and the second online interaction service log in sequence.

4. The method of claim 3, wherein the enabling a first feature mining operation on a first online interactive service log and a second online interactive service log of the digitized interactive service log respectively to obtain a first transitional interaction behavior intention distribution corresponding to the first online interactive service log and the second online interactive service log in sequence comprises: respectively starting feature mining operation aiming at the first online interactive service log and the second online interactive service log by means of a first set feature mining thread and a first feature mining period so as to obtain first transition interactive behavior intention distribution sequentially corresponding to the first online interactive service log and the second online interactive service log;

wherein, the respectively starting second feature mining operation for the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log to obtain a second transitional interaction behavior intention distribution of a dynamic layer corresponding to the first online interaction service log and the second online interaction service log in sequence comprises: and starting feature mining operation aiming at the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log on the basis of a plurality of set first expansion coefficients with comparison results in sequence to obtain second transitional interaction behavior intention distribution sequentially corresponding to the plurality of first expansion coefficients.

5. The method of claim 3, wherein the enabling loss detection operation for the second transitional interaction behavior intention distributions of the respective layers of the first online interaction service log and the second online interaction service log to obtain a first interaction behavior intention distribution corresponding to the first online interaction service log and the second online interaction service log in turn comprises:

sequentially integrating second transitional interactive behavior intention distributions of a plurality of layers of the first online interactive service log to obtain a first integrated interactive behavior intention distribution, and sequentially integrating second transitional interactive behavior intention distributions of a plurality of layers of the second online interactive service log to obtain a second integrated interactive behavior intention distribution;

enabling feature mining operations for the first and second integrated interaction behavior intent distributions, respectively;

and enabling a weighting operation for the first transitional interactive behavior intention distribution of the first online interactive service log and the first integrated interactive behavior intention distribution after the characteristic mining operation to obtain the first interactive behavior intention distribution of the first online interactive service log, and enabling a weighting operation for the first transitional interactive behavior intention distribution of the second online interactive service log and the second integrated interactive behavior intention distribution after the characteristic mining operation to obtain the first interactive behavior intention distribution of the second online interactive service log.

6. The method of claim 2, wherein extracting a first potential interest description distribution of the digitized interaction traffic log and associating a second interaction behavior intent distribution of traffic session description content and potential interest content of the digitized interaction traffic log comprises;

splicing the first online interactive service log and the second online interactive service log to obtain a spliced service log;

enabling at least one third feature mining operation to the splicing service log to obtain a first transition potential interaction behavior intention distribution;

enabling a fourth feature mining operation for the first transitional potential interactive behavior intent distribution to obtain a second transitional potential interactive behavior intent distribution for several levels;

and starting loss detection operation on the second transition potential interest content and the first transition potential interest description distribution, respectively obtaining first potential interest description distribution of the first online interaction business log and the second online interaction business log, and extracting the second interaction behavior intention distribution according to one third feature mining operation.

7. The method of claim 6, wherein enabling no less than one third feature mining operation on the stitched service log results in a first transitional potential interaction behavior intent distribution comprising:

enabling at least one feature mining operation on the splicing business log by means of a second set feature mining thread and a second feature mining period to obtain the first transition potential interaction behavior intention distribution.

8. The method of claim 6, wherein said enabling a fourth feature mining operation for the first transitional potential interactive behavior intent distribution to obtain a second transitional potential interactive behavior intent distribution for several levels comprises:

and based on a plurality of set second expansion coefficients with comparison results, enabling feature mining operation for the first transition potential interactive behavior intention distribution to obtain a second transition potential interactive behavior intention distribution corresponding to the plurality of second expansion coefficients in sequence.

9. The method of claim 2, wherein the performing a key description correlation operation on the digitized interaction service log, the first interaction behavior intent distribution, the first potential interest description distribution, and the second interaction behavior intent distribution of the digitized interaction service log to obtain a correlated interaction behavior intent distribution of the digitized interaction service log comprises: enabling optimization operation on a second online interaction service log through first potential interest description distribution of the first online interaction service log in the digital interaction service log, extracting a first constraint service log content set, and enabling optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log, and extracting a positioning content set of the second online interaction service log; respectively extracting transition associated description of each online interactive service log in the digital interactive service log based on an optimized content set and a positioning content set corresponding to each online interactive service log in the digital interactive service log; extracting potential interest content association records of each online interaction service log of the digital interaction service log according to first potential interest description distribution and second interaction behavior intention distribution of each online interaction service log in the digital interaction service log; correspondingly obtaining the associated interactive behavior intention distribution of each online interactive service log according to the first interactive behavior intention distribution of the first online interactive service log of each online interactive service log in the digital interactive service log, the transition associated interactive behavior intention distribution of the first online interactive service log and the integration result of the potential interest content associated records of the first online interactive service log;

the enabling of optimization operation on a second online interaction service log through a first potential interest description distribution of the first online interaction service log in the digital interaction service log and extracting the first constraint service log content set, and the enabling of optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log and extracting the positioning content set of the second online interaction service log comprise: enabling an alignment process for a second online interaction service log by means of a first potential interest description distribution of the first online interaction service log in the digitized interaction service log to obtain an optimized content set of the first online interaction service log, and enabling an alignment process for the first online interaction service log by means of the first potential interest description distribution of the second online interaction service log to obtain an optimized content set of the second online interaction service log; respectively obtaining the positioning content sets of the first online interactive service log and the second online interactive service log according to the comparison result between each online interactive service log in the digital interactive service log and the corresponding optimized content set;

wherein, based on the optimized content set and the positioning content set corresponding to each online interactive service log in the digital interactive service log, respectively extracting the transition associated description of each online interactive service log in the digital interactive service log, including: obtaining the transitional association interaction behavior intention distribution of the first online interaction service log based on the optimized content set of the first online interaction service log and the positioning content set of the first online interaction service log according to a first set strategy; and obtaining the transitional association interaction behavior intention distribution of the second online interaction service log based on the optimized content set of the second online interaction service log and the positioning content set of the second online interaction service log according to a second set strategy.

10. A business information processing server comprising a processing engine, a network module and a memory, the processing engine and the memory communicating through the network module, the processing engine being configured to read a computer program from the memory and to execute the computer program to implement the method of any one of claims 1 to 9.

Technical Field

The invention relates to the technical field of service information processing, in particular to a service information processing method and a server based on time sequence characteristics.

Background

The continuous progress of scientific technology promotes various services to be continuously upgraded and transformed to a digital mode, so that the time limit and the region limit of the traditional service handling can be broken through, the flexibility of the service handling is improved, and unnecessary resource waste is reduced. With the continuous expansion of the digital business scale, in order to improve the continuous upgrading optimization of the digital business service, the requirement mining and analysis are generally required to be performed on business information, but the inventor finds that the business information used for performing the requirement mining and analysis is often low in feature recognition degree in the research process, but the prior art is difficult to effectively overcome the problems.

Disclosure of Invention

In a first aspect, an embodiment of the present invention provides a service information processing method based on a time sequence characteristic, which is applied to a service information processing server, and the method at least includes: determining a digital interactive service log, wherein the digital interactive service log comprises a first online interactive service log and a second online interactive service log which point to the same service session event with time sequence characteristics and are crawled in the same service session service state; extracting a first interaction behavior intention distribution of the digital interaction service log, a first potential interest description distribution of the digital interaction service log, and a second interaction behavior intention distribution associating service session description content and potential interest content of the digital interaction service log; performing key description association operation on the digital interaction service log, a first interaction behavior intention distribution, a first potential interest description distribution and a second interaction behavior intention distribution of the digital interaction service log to obtain an associated interaction behavior intention distribution of the digital interaction service log; starting an updating operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the characteristic identification degree correction operation; the starting of the updating operation on the distribution of the associated interaction behavior intention of the digital interaction service log to obtain the digital interaction service log after the characteristic identification degree correction operation comprises the following steps: and respectively starting feature mining operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the feature recognition degree correction operation.

In some preferred embodiments, the extracting the first interaction behavior intention distribution of the digitized interaction service log includes: respectively starting first feature mining operation aiming at the first online interactive service log and the second online interactive service log to obtain first transition interactive behavior intention distribution corresponding to the first online interactive service log and the second online interactive service log in sequence; respectively starting second characteristic mining operation aiming at the first transition interaction behavior intention distribution of the first online interaction service log and the second online interaction service log to obtain second transition interaction behavior intention distribution of a dynamic layer corresponding to the first online interaction service log and the second online interaction service log in sequence; and respectively starting loss detection operation aiming at the second transition interaction behavior intention distribution of each layer of the first online interaction service log and the second online interaction service log to obtain first interaction behavior intention distribution corresponding to the first online interaction service log and the second online interaction service log in sequence.

In some preferred embodiments, the enabling, by the first feature mining operation, a first online interaction service log and a second online interaction service log of the digitized interaction service log respectively to obtain a first transitional interaction behavior intention distribution sequentially corresponding to the first online interaction service log and the second online interaction service log includes: respectively starting feature mining operation aiming at the first online interactive service log and the second online interactive service log by means of a first set feature mining thread and a first feature mining period so as to obtain first transition interactive behavior intention distribution sequentially corresponding to the first online interactive service log and the second online interactive service log.

In some preferred embodiments, the enabling, for the first transitional interaction behavior intention distributions of the first online interaction service log and the second online interaction service log, a second feature mining operation to obtain a second transitional interaction behavior intention distribution of a dynamic level sequentially corresponding to the first online interaction service log and the second online interaction service log respectively includes: and starting feature mining operation aiming at the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log on the basis of a plurality of set first expansion coefficients with comparison results in sequence to obtain second transitional interaction behavior intention distribution sequentially corresponding to the plurality of first expansion coefficients.

In some preferred embodiments, the enabling loss detection operation for the second transitional interaction behavior intention distributions of the respective layers of the first online interaction service log and the second online interaction service log respectively to obtain a first interaction behavior intention distribution corresponding to the first online interaction service log and the second online interaction service log in turn includes: sequentially integrating second transitional interactive behavior intention distributions of a plurality of layers of the first online interactive service log to obtain a first integrated interactive behavior intention distribution, and sequentially integrating second transitional interactive behavior intention distributions of a plurality of layers of the second online interactive service log to obtain a second integrated interactive behavior intention distribution; enabling feature mining operations for the first and second integrated interaction behavior intent distributions, respectively; and enabling a weighting operation for the first transitional interactive behavior intention distribution of the first online interactive service log and the first integrated interactive behavior intention distribution after the characteristic mining operation to obtain the first interactive behavior intention distribution of the first online interactive service log, and enabling a weighting operation for the first transitional interactive behavior intention distribution of the second online interactive service log and the second integrated interactive behavior intention distribution after the characteristic mining operation to obtain the first interactive behavior intention distribution of the second online interactive service log.

In some preferred embodiments, extracting a first potential interest description distribution of the digitized interaction service log, and associating a second interaction behavior intention distribution of the service session description content and the potential interest content of the digitized interaction service log, including; splicing the first online interactive service log and the second online interactive service log to obtain a spliced service log; enabling at least one third feature mining operation to the splicing service log to obtain a first transition potential interaction behavior intention distribution; enabling a fourth feature mining operation for the first transitional potential interactive behavior intent distribution to obtain a second transitional potential interactive behavior intent distribution for several levels; and starting loss detection operation on the second transition potential interest content and the first transition potential interest description distribution, respectively obtaining first potential interest description distribution of the first online interaction business log and the second online interaction business log, and extracting the second interaction behavior intention distribution according to one third feature mining operation.

In some preferred embodiments, the enabling no less than one third feature mining operation on the stitched service log to obtain a first transitional potential interaction behavior intention distribution includes: enabling at least one feature mining operation on the splicing business log by means of a second set feature mining thread and a second feature mining period to obtain the first transition potential interaction behavior intention distribution.

In some preferred embodiments, the enabling a fourth feature mining operation for the first transitional potential interactive behavior intent distribution to obtain several levels of second transitional potential interactive behavior intent distributions includes: and based on a plurality of set second expansion coefficients with comparison results, enabling feature mining operation for the first transition potential interactive behavior intention distribution to obtain a second transition potential interactive behavior intention distribution corresponding to the plurality of second expansion coefficients in sequence.

In some preferred embodiments, the performing a key description association operation on the digital interaction service log, the first interaction behavior intention distribution, the first potential interest description distribution, and the second interaction behavior intention distribution of the digital interaction service log to obtain an associated interaction behavior intention distribution of the digital interaction service log includes: enabling optimization operation on a second online interaction service log through first potential interest description distribution of the first online interaction service log in the digital interaction service log, extracting a first constraint service log content set, and enabling optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log, and extracting a positioning content set of the second online interaction service log; respectively extracting transition associated description of each online interactive service log in the digital interactive service log based on an optimized content set and a positioning content set corresponding to each online interactive service log in the digital interactive service log; extracting potential interest content association records of each online interaction service log of the digital interaction service log according to first potential interest description distribution and second interaction behavior intention distribution of each online interaction service log in the digital interaction service log; correspondingly obtaining the associated interactive behavior intention distribution of each online interactive service log according to the first interactive behavior intention distribution of the first online interactive service log of each online interactive service log in the digital interactive service log, the transition associated interactive behavior intention distribution of the first online interactive service log and the integration result of the potential interest content associated records of the first online interactive service log;

the enabling of optimization operation on a second online interaction service log through a first potential interest description distribution of the first online interaction service log in the digital interaction service log and extracting the first constraint service log content set, and the enabling of optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log and extracting the positioning content set of the second online interaction service log comprise: enabling an alignment process for a second online interaction service log by means of a first potential interest description distribution of the first online interaction service log in the digitized interaction service log to obtain an optimized content set of the first online interaction service log, and enabling an alignment process for the first online interaction service log by means of the first potential interest description distribution of the second online interaction service log to obtain an optimized content set of the second online interaction service log; and respectively obtaining the positioning content sets of the first online interactive service log and the second online interactive service log according to the comparison result between each online interactive service log in the digital interactive service log and the corresponding optimized content set.

In some preferred embodiments, the extracting, based on the optimized content set and the positioning content set corresponding to each online interaction service log in the digital interaction service log, a transition association description of each online interaction service log in the digital interaction service log, respectively, includes: obtaining the transitional association interaction behavior intention distribution of the first online interaction service log based on the optimized content set of the first online interaction service log and the positioning content set of the first online interaction service log according to a first set strategy; and obtaining the transitional association interaction behavior intention distribution of the second online interaction service log based on the optimized content set of the second online interaction service log and the positioning content set of the second online interaction service log according to a second set strategy.

In a second aspect, an embodiment of the present invention further provides a service information processing server, including a processing engine, a network module, and a memory, where the processing engine and the memory communicate through the network module, and the processing engine is configured to read a computer program from the memory and execute the computer program, so as to implement the foregoing method.

Applied to the embodiment of the application, the digital interactive service log can be taken as raw material information, behavior intention mining operation is respectively started on a first online interactive service log and a second online interactive service log in the digital interactive service log to obtain corresponding first interactive behavior intention distribution, and can extract the potential interest description distribution of the first online interactive service log and the second online interactive service log in the digital interactive service log, the extracted intent description and interest description may then be correlated to obtain an associated description of the business log content and potential preferences, the association description covers more complete and diversified log data, and can deal with the interference of conversation scene updating, reduce the error of behavior intention mining, and then, updating operation is started for the association description so as to obtain the digital interactive service log with higher feature recognition degree. The embodiment of the application carries out the characteristic identification degree correction operation on the digital interactive service log, can improve the characteristic identification degree of the online interactive service log, and thus can ensure that the online interactive service log can be suitable for as many demand analysis tasks as possible.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

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

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.

Fig. 1 is a block diagram illustrating an application scenario of an exemplary time-series feature-based service information processing method according to some embodiments of the present invention.

Fig. 2 is a schematic diagram illustrating hardware and software components in an exemplary business information processing server according to some embodiments of the present invention.

Fig. 3 is a flow diagram illustrating an exemplary timing feature based traffic information processing method and/or process according to some embodiments of the invention.

Detailed Description

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the invention.

These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure disclosed in the present application, and the economics of production may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.

The present invention uses flow charts to illustrate the execution processes performed by a system according to an embodiment of the present invention. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.

Fig. 1 is a block diagram illustrating an exemplary service information processing server 300 according to some embodiments of the present invention, and the service information processing server 300 may include a service information processing server 100 and a session client 200.

In some embodiments, as shown in fig. 2, the service information processing server 100 may include a processing engine 110, a network module 120, and a memory 130, the processing engine 110 and the memory 130 communicating through the network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described in this disclosure. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It is understood that the structure shown in fig. 2 is only an illustration, and the service information processing server 100 may further include more or less components than those shown in fig. 2, or have a different configuration from that shown in fig. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Fig. 3 is a flowchart illustrating an exemplary time-series characteristic-based service information processing method and/or process, which is applied to the service information processing server 100 in fig. 1, according to some embodiments of the present invention, and further may include the technical solutions described in the following.

Step 100, determining a digital interactive service log.

In the embodiment of the application, the digital interactive service log comprises a first online interactive service log and a second online interactive service log which point to the same service session event with time sequence characteristics and are crawled in the same service session service state. For example, the business session event having the time sequence feature may be a payment session event, an office session event, a smart medical event, a corporate cloud service event, and the like, and the time sequence feature may be understood as a time sequence. The service state of the business session can be a real-time state or a delay state, and the first online interactive business log and the second online interactive business log which are crawled can be different online interactive business logs. The online interaction service log in the embodiment of the present application may be a text log or a graphical log, but is not limited thereto.

Step 200, extracting a first interaction behavior intention distribution of the digital interaction service log, a first potential interest description distribution of the digital interaction service log, and a second interaction behavior intention distribution associating service session description content and potential interest content of the digital interaction service log.

In the embodiment of the application, the interactive behavior intention distribution can be understood as an interactive behavior intention set, the interactive behavior intention comprises various interest intentions or interest preferences, and the potential interest description distribution can be understood as a potential interest description set. From another perspective, the interactive behavior intention distribution, the potential interest description distribution, and the business session description content can be understood as features of different dimensions of the business log.

In the embodiment of the present application, the extracting the first interaction behavior intention distribution of the digitized interaction service log described in step 200 may include the following technical solutions described in steps 210 to 230.

Step 210, respectively starting a first feature mining operation for the first online interactive service log and the second online interactive service log to obtain a first transitional interactive behavior intention distribution corresponding to the first online interactive service log and the second online interactive service log in sequence.

In the embodiment of the present application, the feature mining operation may be understood as a convolution operation, and the transitional interaction behavior intention distribution may be understood as an intermediate type interaction behavior intention distribution.

In some possible embodiments, the enabling of the first feature mining operation on the first online interaction service log and the second online interaction service log of the digitized interaction service log to obtain the first transitional interaction behavior intention distribution corresponding to the first online interaction service log and the second online interaction service log in turn, which is described in step 210, may include the following: respectively starting feature mining operation aiming at the first online interactive service log and the second online interactive service log by means of a first set feature mining thread and a first feature mining period so as to obtain first transition interactive behavior intention distribution sequentially corresponding to the first online interactive service log and the second online interactive service log.

For example, the feature mining period may be a feature mining interval or a feature mining step size. In this way, by combining feature mining cycles, the orderliness of the transitional interaction behavior intention distribution can be ensured.

Step 220, respectively starting second feature mining operation for the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log to obtain a second transitional interaction behavior intention distribution of a dynamic layer corresponding to the first online interaction service log and the second online interaction service log in sequence.

It is understood that the dynamic level may include multiple levels. In some possible embodiments, the enabling of the second feature mining operation for the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log described in step 220 to obtain a second transitional interaction behavior intention distribution of a dynamic level corresponding to the first online interaction service log and the second online interaction service log in turn may include the following: and starting feature mining operation aiming at the first transitional interaction behavior intention distribution of the first online interaction service log and the second online interaction service log on the basis of a plurality of set first expansion coefficients with comparison results in sequence to obtain second transitional interaction behavior intention distribution sequentially corresponding to the plurality of first expansion coefficients.

In the embodiment of the application, the expansion coefficient may be an expansion rate, and by designing the expansion coefficient, the feature recognition degree of the transitional interaction behavior intention distribution can be ensured, and confusion among the transitional interaction behavior intention distributions is avoided.

Step 230, respectively starting loss detection operation for second transitional interaction behavior intention distributions of each layer of the first online interaction service log and the second online interaction service log to obtain first interaction behavior intention distributions corresponding to the first online interaction service log and the second online interaction service log in sequence.

In the embodiment of the present application, the loss detection operation may be understood as a residual operation. By means of the design, the integrity of the interactive behavior intention distribution can be ensured by combining the characteristic mining operation and the residual error operation.

In some possible embodiments, the second transitional interaction behavior intention distribution for each layer of the first online interaction service log and the second online interaction service log, which is described in step 230, respectively enables a loss detection operation to obtain a first interaction behavior intention distribution to which the first online interaction service log and the second online interaction service log sequentially correspond, which may include the technical solutions described in the following steps 231 to 233.

And 231, sequentially integrating the second transitional interactive behavior intention distributions of the plurality of layers of the first online interactive service log to obtain a first integrated interactive behavior intention distribution, and sequentially integrating the second transitional interactive behavior intention distributions of the plurality of layers of the second online interactive service log to obtain a second integrated interactive behavior intention distribution.

And 232, respectively enabling feature mining operation aiming at the first integrated interactive behavior intention distribution and the second integrated interactive behavior intention distribution.

Step 233, a weighting operation is enabled for the first transitional interaction behavior intention distribution of the first online interaction service log and the first integrated interaction behavior intention distribution after the feature mining operation to obtain the first interaction behavior intention distribution of the first online interaction service log, and a weighting operation is enabled for the first transitional interaction behavior intention distribution of the second online interaction service log and the second integrated interaction behavior intention distribution after the feature mining operation to obtain the first interaction behavior intention distribution of the second online interaction service log.

The steps 231 to 233 are applied to ensure the richness of the distribution of the interactive behavior intention as much as possible by integrating the distribution of the interactive behavior intention at different levels.

In some possible embodiments, the extracting of the first potential interest description distribution of the digital interaction service log and the associating of the service session description content and the second interaction behavior intention distribution of the potential interest content of the digital interaction service log, which are described in step 200, may include the following technical solutions described in steps 241 to 244.

And 241, splicing the first online interactive service log and the second online interactive service log to obtain a spliced service log.

In this embodiment of the present application, the splicing service log may be a splicing service log obtained after fusing the first online interaction service log and the second online interaction service log.

And 242, enabling at least one third feature mining operation to the splicing service log to obtain a first transition potential interaction behavior intention distribution.

Further, the enabling of the third feature mining operation not less than one time to the stitched service log to obtain the first transitional potential interaction behavior intention distribution as described in step 242 may include the following: enabling at least one feature mining operation on the splicing business log by means of a second set feature mining thread and a second feature mining period to obtain the first transition potential interaction behavior intention distribution.

And 243, enabling a fourth feature mining operation for the first transition potential interactive behavior intention distribution to obtain a second transition potential interactive behavior intention distribution of several levels.

In some possible embodiments, enabling a fourth feature mining operation for the first transitional potential interactive behavior intent distribution to obtain several levels of second transitional potential interactive behavior intent distributions, as described in step 243, may include the following: and based on a plurality of set second expansion coefficients with comparison results, enabling feature mining operation for the first transition potential interactive behavior intention distribution to obtain a second transition potential interactive behavior intention distribution corresponding to the plurality of second expansion coefficients in sequence.

And 244, starting loss detection operation on the second transition potential interest content and the first transition potential interest description distribution, respectively obtaining the first potential interest description distribution of the first online interactive service log and the second online interactive service log, and extracting the second interactive behavior intention distribution according to a third feature mining operation of the first online interactive service log and the second online interactive service log.

It can be understood that when the technical solutions described in steps 241 to 244 are applied, the integrity and accuracy of the distribution of the first potential interest description and the distribution of the second interactive behavior intention can be ensured.

Step 300, performing key description association operation on the digital interaction service log, the first interaction behavior intention distribution, the first potential interest description distribution and the second interaction behavior intention distribution of the digital interaction service log to obtain an associated interaction behavior intention distribution of the digital interaction service log; and starting an updating operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the characteristic identification degree correction operation.

In some possible embodiments, the key description association operation may be understood as key description fusion, so that different descriptions can be integrated, and the feature recognition degree of the digital interactive service log is optimized through feature recognition degree correction, for example, some noise contents with low feature recognition degree are cleaned, so as to ensure the data information quality of the digital interactive service log after the feature recognition degree correction operation.

In some possible embodiments, the enabling of the update operation for the distribution of the associated interaction behavior intention of the digitized interaction service log described in step 300 to obtain the digitized interaction service log after the feature recognition degree correction operation may include the following: and respectively starting feature mining operation on the associated interaction behavior intention distribution of the digital interaction service log to obtain the digital interaction service log after the feature recognition degree correction operation.

In other possible embodiments, the performing a key description association operation on the digitized interaction service log, the first interaction behavior intention distribution, the first potential interest description distribution, and the second interaction behavior intention distribution of the digitized interaction service log, which are described in step 300, to obtain an associated interaction behavior intention distribution of the digitized interaction service log may include the following technical solutions described in steps 310 to 340.

Step 310, enabling optimization operation on a second online interaction service log through a first potential interest description distribution of the first online interaction service log in the digital interaction service log, extracting a first constraint service log content set, and enabling optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log, and extracting a positioning content set of the second online interaction service log.

In the embodiment of the present application, the constrained service log content set may be understood as a part of content in the corresponding online interactive service log, and the positioning content set may be understood as a part of content in the corresponding online interactive service log. For example, the optimization operation may be understood as a positioning process.

In some possible embodiments, the step 310 of enabling an optimization operation on the second online interaction service log through the first potential interest description distribution of the first online interaction service log in the digital interaction service log, extracting the first constraint service log content set, and enabling an optimization operation on the first online interaction service log through the first potential interest description distribution of the second online interaction service log in the digital interaction service log, extracting the location content set of the second online interaction service log may include the technical solutions described in the steps 311 and 312.

Step 311, enabling an alignment process for a second online interaction service log by means of a first potential interest description distribution of the first online interaction service log in the digitized interaction service log to obtain an optimized content set of the first online interaction service log, and enabling an alignment process for the first online interaction service log by means of the first potential interest description distribution of the second online interaction service log to obtain an optimized content set of the second online interaction service log.

And step 312, obtaining the positioning content sets of the first online interactive service log and the second online interactive service log respectively according to the comparison result between each online interactive service log in the digital interactive service log and the corresponding optimized content set.

And 320, respectively extracting transition associated description of each online interactive service log in the digital interactive service log based on the optimized content set and the positioning content set corresponding to each online interactive service log in the digital interactive service log.

In some possible embodiments, the extracting the transition association description of each online interaction service log in the digital interaction service log based on the optimized content set and the positioning content set corresponding to each online interaction service log in the digital interaction service log in step 320 may include the following steps: obtaining the transitional association interaction behavior intention distribution of the first online interaction service log based on the optimized content set of the first online interaction service log and the positioning content set of the first online interaction service log according to a first set strategy; and obtaining the transitional association interaction behavior intention distribution of the second online interaction service log based on the optimized content set of the second online interaction service log and the positioning content set of the second online interaction service log according to a second set strategy.

It is to be understood that the first setting policy and the second setting policy may be understood as describing an extraction rule or a feature identification indication, and the embodiment of the present application is not limited thereto.

Step 330, extracting the potential interest content association record of each online interaction service log of the digital interaction service log according to the first potential interest description distribution and the second interaction behavior intention distribution of each online interaction service log in the digital interaction service log.

Step 340, correspondingly obtaining the associated interactive behavior intention distribution of each online interactive service log according to the first interactive behavior intention distribution of the first online interactive service log of each online interactive service log in the digital interactive service log, the transition associated interactive behavior intention distribution of the first online interactive service log and the integration result of the potential interest content associated records of the first online interactive service log.

By designing in this way, when the technical solutions described in steps 310-340 are applied, the integrity and content richness of the distribution of the associated interaction behavior intention can be ensured.

Applied to the embodiment of the application, the digital interactive service log can be taken as raw material information, behavior intention mining operation is respectively started on a first online interactive service log and a second online interactive service log in the digital interactive service log to obtain corresponding first interactive behavior intention distribution, and can extract the potential interest description distribution of the first online interactive service log and the second online interactive service log in the digital interactive service log, the extracted intent description and interest description may then be correlated to obtain an associated description of the business log content and potential preferences, the association description covers more complete and diversified log data, and can deal with the interference of conversation scene updating, reduce the error of behavior intention mining, and then, updating operation is started for the association description so as to obtain the digital interactive service log with higher feature recognition degree. The embodiment of the application carries out the characteristic identification degree correction operation on the digital interactive service log, can improve the characteristic identification degree of the online interactive service log, and thus can ensure that the online interactive service log can be suitable for as many demand analysis tasks as possible.

The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of unexplained technical feature terms, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.

The foregoing disclosure of embodiments of the present invention will be apparent to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present invention, and thus the above contents are not an inventive judgment of the overall scheme.

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. Various modifications, improvements and adaptations to the present invention may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the present invention uses specific terms to describe embodiments of the present invention. Such as "one embodiment," "an embodiment," and/or "some embodiments" means a feature, structure, or characteristic described in connection with at least one embodiment of the invention. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some of the features, structures, or characteristics of at least one embodiment of the present invention may be combined as suitable.

In addition, those skilled in the art will recognize that the various aspects of the invention may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, articles of manufacture, or materials, or any new and useful modifications thereto. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present invention may be embodied as a computer product, located in at least one computer-readable medium, comprising computer-readable program code.

A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the execution of aspects of the present invention may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, 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).

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

It should also be appreciated that in the foregoing description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to suggest that the claimed subject matter requires more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.