应用于人工智能的大数据维护方法及大数据服务器

Big data maintenance method applied to artificial intelligence and big data server

文档序号：7517 发布日期：2021-09-17 浏览：26次中文

1. A big data maintenance method applied to artificial intelligence is characterized by being applied to a big data server and comprising the following steps:

based on the service data maintenance list, performing service data correction on the acquired associated service data to obtain service data to be maintained, wherein the service data to be maintained is matched with a prestored reference data format;

determining a service data calling detection result of the service data to be maintained in a mode of carrying out service data calling detection on the service data to be maintained;

extracting a service data cluster set of service data to be maintained based on the service data calling detection result;

and deleting, reconstructing and expanding the service data to be maintained according to the service data cluster set.

2. The method of claim 1, wherein performing service data correction on the obtained associated service data to obtain service data to be maintained, which is matched with a pre-stored reference data format, comprises:

determining four service dimension information of the calibration service data corresponding to the service data label in the associated service data;

determining an information matching list of four service dimension information of the calibration service data corresponding to the service data label and four service dimension information of the reference data format;

performing attribute feature mapping on the tag attribute features of the service data tags according to the information matching list to obtain mapping attribute features;

and determining the service data to be maintained according to the mapping attribute characteristics.

3. The method of claim 2, wherein determining four service dimension information of the calibration service data corresponding to the service data tag in the associated service data comprises:

extracting attribute features of the associated service data based on an artificial intelligence feature extraction network model to obtain tag attribute features of a service data tag;

extracting attribute feature description information of the label attribute features of the service data labels, converting the attribute feature description information through a preset description information mapping matrix to obtain four target description information, and determining four information subsets with description value updating marks of the four target description information as four service dimension information of the calibration service data corresponding to the service data labels.

4. The method of claim 2, wherein determining the service data to be maintained according to the mapping attribute characteristics comprises:

acquiring an attribute feature sequence with the maximum attribute feature value in the mapping attribute features through a preset attribute feature value identification model;

determining the relative feature matching degree of the attribute feature sequence and the last attribute feature description information of the tag attribute feature of the service data tag;

if the relative feature matching degree is smaller than half of the dynamic feature matching degree of the mapping attribute feature, performing recombination service data correction in a manner of performing feature recombination on the mapping attribute feature to obtain the service data to be maintained;

and if the relative feature matching degree is greater than half of the dynamic feature matching degree of the mapping attribute feature, determining the mapping attribute feature as the service data to be maintained.

5. The method of claim 1, wherein extracting a service data cluster set of service data to be maintained based on the service data call detection result comprises:

setting the data format index information in the service data to be maintained according to a data format index information preset in a reference data format;

and comparing the service data calling detection result with the data format index information to determine a service data cluster set of the service data to be maintained.

6. The method of claim 5, wherein comparing the service data call detection result with the data format indicator information to determine the service data cluster set of the service data to be maintained comprises:

determining a first data format comparison result of current data format index information, first data format index information and fifth data format index information of a data calling detection record of the service data calling detection result, and determining service data behavior elements in a service data clustering set of the service data to be maintained according to the first data format comparison result; the first data format index information is target description information corresponding to last attribute feature description information of a tag attribute feature of a service data tag between a service data behavior element and a service data type element, and the fifth data format index information is target description information corresponding to the first data format index information between the service data type element and a service data object element;

determining a second data format comparison result of current data format index information of a data calling detection record of the service data calling detection result and first data format index information, second data format index information and fifth data format index information, and determining a service data type element and a service data object element in a service data clustering set of the service data to be maintained according to the second data format comparison result; the second data format index information is target description information between a service data type element and a service data demand element and corresponding to last attribute feature description information of the tag attribute feature of the service data tag;

determining a third data format comparison result of current data format index information and second data format index information, third data format index information and fifth data format index information of a data calling detection record of the service data calling detection result, and determining a service data demand element in a service data clustering set of the service data to be maintained according to the third data format comparison result; the third data format index information is target description information between a service data demand element and a service data associated element and corresponding to last attribute feature description information of the tag attribute feature of the service data tag;

determining a fourth data format comparison result of current data format index information and third data format index information, fourth data format index information and fifth data format index information of a data calling detection record of the service data calling detection result, and determining service data associated elements in a service data clustering set of the service data to be maintained according to the fourth data format comparison result;

determining a fifth data format comparison result of current data format index information, fourth data format index information and fifth data format index information of a data calling detection record of the service data calling detection result, and determining a service data label sequence element in a service data clustering set of the service data to be maintained according to the fifth data format comparison result; the fourth data format index information is target description information between the service data associated element and the service data label sequence element and corresponding to the last attribute feature description information of the label attribute feature of the service data label.

7. The method according to claim 1, wherein a service data set for each service processing overlapping period is stored in the big data server, the service processing overlapping period is an overlapping period of at least two different service processing requests, the service data set includes service data processing records of preset service processing threads corresponding to different service processing categories for each service processing request in the service processing overlapping period, and the service data maintenance list is determined by:

receiving service operation data of a current user service terminal, wherein the service operation data comprises a service operation time interval, a service operation behavior tag and service operation feedback information of the current user service terminal;

judging whether the service operation time interval is in a service processing overlapping time interval or not; if the service operation time interval is in a service processing overlapping time interval, acquiring a target service data processing record of a preset service processing thread of each service processing request corresponding to the service operation feedback information in the service processing overlapping time interval from the service data set of the service processing overlapping time interval;

and generating a service data maintenance list corresponding to the current user service terminal according to the target service data processing record corresponding to each acquired service processing request and the service operation behavior label.

8. A big data server is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory are communicated through the network module, and the processing engine reads a computer program from the memory and runs the computer program to execute the big data maintenance method applied to artificial intelligence in any one of claims 1 to 7.

Background

With the development of new generation information technology, Big Data (Big Data) plays a crucial role in the social development today. Many industries begin to perform online business processing by means of big data, so that not only can the regional limitation and the time limitation of business processing be broken, but also the efficiency of business processing can be improved.

However, in the actual service handling process, as the number of the user service terminals increases, the service processing requests corresponding to the user service terminals also increase rapidly, which may cause the big data server to confuse the service data corresponding to different user service terminals during service processing, thereby causing confusion of the service processing results received by the user service terminals.

Disclosure of Invention

The first aspect of the present application discloses a big data maintenance method applied to artificial intelligence, which is applied to a big data server, wherein a service data set of each service processing overlapping time interval is stored in the big data server, the service processing overlapping time interval is an overlapping time interval of at least two different service processing requests, the service data set includes service data processing records of each preset service processing thread corresponding to each service processing request in different service processing categories in the service processing overlapping time interval, and the method includes:

generating a service data maintenance list corresponding to the current user service terminal according to the obtained target service data processing record corresponding to each service processing request and the service operation behavior label;

and performing data maintenance on the associated service data corresponding to the service operation data based on the service data maintenance list.

In a preferred embodiment, the method further includes a step of predetermining a service data set of each service processing overlapping time period, and specifically includes:

acquiring a first service scene data set of each service processing scene, wherein the first service scene data set comprises service processing category information, object tag information, object interaction record information, object state information and a corresponding service processing request of each service scene object;

screening the service scene data of the first service scene data set of each service processing scene, and determining a second service scene data set of each service processing request in each service processing overlapping time period;

aiming at each service processing overlapping time period, respectively generating a third service scene data set of each service processing request according to different service processing categories according to the second service scene data set of each service processing request in the service processing overlapping time period;

and determining different set time periods according to the third service scene data set of each service processing request, and calculating the service data processing record of each preset service processing thread corresponding to each service processing request so as to determine the service data set of the service processing overlapping time period.

In a preferred embodiment, the step of performing service context data screening on the first service context data set of each service processing context and determining the second service context data set of each service processing request in each service processing overlapping period includes:

determining a service processing scene corresponding to each service scene object according to the object state information of each service scene object;

and screening the service scene data of the first service scene data set of each service processing scene according to the service processing scene corresponding to each service scene object and the corresponding service processing request, and determining a second service scene data set of each service processing request in each service processing overlapping period.

In a preferred embodiment, the step of determining different set time periods according to the third service scenario data set of each service processing request includes:

calculating real-time object interaction records of all service scene objects in the third service scene data set of each service processing request in each set interaction record statistical time period aiming at the third service scene data set of each service processing request;

for each set interaction record statistical time period, judging whether the data updating index of the service interaction description data of the real-time object interaction record of each service scene object in the set interaction record statistical time period is greater than the data updating index of the preset service interaction description data;

if the data updating index of the service interaction description data recorded by the real-time object interaction is larger than the data updating index of the preset service interaction description data, determining the set interaction record statistical time period as a first target statistical time period;

if the data updating index of the service interaction description data recorded by the real-time object interaction is not greater than the data updating index of the preset service interaction description data, determining the set interaction record statistical time period as a second target statistical time period;

and setting each first target statistical time period as a different set time period, correcting the plurality of second target statistical time periods according to preset time period correction indexes to obtain at least one correction time period, and setting each correction time period as one set time period.

In a preferred embodiment, each service processing overlapping period includes a first service processing request and a second service processing request, and the step of generating a service data maintenance list corresponding to the current user service terminal according to the obtained target service data processing record corresponding to each service processing request and the service operation behavior tag includes:

calculating a characteristic change track of a service interaction record characteristic of a first object interaction record between the service operation behavior tag and a target service data processing record corresponding to the first service processing request and a characteristic change track of a service interaction record characteristic of a second object interaction record between a target service data processing record corresponding to the second service processing request;

determining a service data maintenance list corresponding to the current user service terminal according to the characteristic change track of the service interaction record characteristic of the first object interaction record and the characteristic change track of the service interaction record characteristic of the second object interaction record;

wherein, the step of determining the service data maintenance list corresponding to the current user service terminal according to the feature change trajectory of the service interaction record feature of the first object interaction record and the feature change trajectory of the service interaction record feature of the second object interaction record comprises:

judging whether the track change index weight of the characteristic change track of the service interaction record characteristic of the first object interaction record is greater than the track change index weight of the characteristic change track of the service interaction record characteristic of the second object interaction record;

if the track change index weight of the characteristic change track of the service interaction record characteristic of the first object interaction record is greater than the track change index weight of the characteristic change track of the service interaction record characteristic of the second object interaction record, determining a service data maintenance list corresponding to the current user service terminal according to the second service processing request;

if the track change index weight of the characteristic change track of the service interaction record characteristic of the first object interaction record is less than or equal to the track change index weight of the characteristic change track of the service interaction record characteristic of the second object interaction record, determining a service data maintenance list corresponding to the current user service terminal according to the first service processing request;

the step of determining the service data maintenance list corresponding to the current user service terminal according to the characteristic change trajectory of the service interaction record characteristic of the first object interaction record and the characteristic change trajectory of the service interaction record characteristic of the second object interaction record includes:

searching for a first service evaluation coefficient corresponding to an index weight interval of the service interaction record characteristic of the first object interaction record corresponding to the characteristic change track of the service interaction record characteristic of the first object interaction record and a second service evaluation coefficient corresponding to an index weight interval of the service interaction record characteristic of the second object interaction record corresponding to the characteristic change track of the service interaction record characteristic of the second object interaction record;

respectively judging whether the first service evaluation coefficient and the second service evaluation coefficient are greater than a set service evaluation coefficient;

if the first service evaluation coefficient is greater than the set service evaluation coefficient, determining a service data maintenance list corresponding to the current user service terminal according to the first service processing request;

if the second service evaluation coefficient is greater than the set service evaluation coefficient, determining a service data maintenance list corresponding to the current user service terminal according to the second service processing request;

if the first service evaluation coefficient and the second service evaluation coefficient are not greater than the set service evaluation coefficient, iteratively updating the service data set of each service processing overlapping time period, and returning to the step of calculating the characteristic change track of the service interaction record characteristic of the first object interaction record between the service operation behavior tag and the target service data processing record corresponding to the first service processing request and the characteristic change track of the service interaction record characteristic of the second object interaction record between the target service data processing records corresponding to the second service processing request.

In a preferred embodiment, the method further includes a step of presetting a plurality of service evaluation coefficients corresponding to each service processing overlapping time period and an index weight interval of a service interaction record feature of an object interaction record corresponding to each service evaluation coefficient, and specifically includes:

calculating the service interaction record characteristics of the object interaction records between the target service data processing records corresponding to the first service processing request and the target service data processing records corresponding to the second service processing request in the service processing overlapping time period aiming at each service processing overlapping time period;

determining a plurality of service evaluation coefficients and a service evaluation time sequence weight corresponding to each service evaluation coefficient according to the service data sets of the service processing overlapped time periods;

and calculating a characteristic weighting result between the service evaluation time sequence weight corresponding to each service evaluation coefficient and the service interaction record characteristic of the object interaction record, and configuring an index weight interval of the service interaction record characteristic of the object interaction record corresponding to each service evaluation coefficient according to the characteristic weighting result between the service evaluation time sequence weight corresponding to each service evaluation coefficient and the service interaction record characteristic of the object interaction record.

In a preferred embodiment, the method further comprises: and sending the service data maintenance list corresponding to the current user service terminal to the associated user service terminal of the current user service terminal.

In a preferred embodiment, the performing data maintenance on the associated service data corresponding to the service operation data based on the service data maintenance list includes:

determining a service data calling detection result of the service data to be maintained in a mode of carrying out service data calling detection on the service data to be maintained;

extracting a service data cluster set of service data to be maintained based on the service data calling detection result;

according to the service data cluster set, performing service data deletion, service data reconstruction and service data expansion on the service data to be maintained;

the step of correcting the acquired associated service data to obtain service data to be maintained, which is matched with a pre-stored reference data format, comprises the following steps: determining four service dimension information of the calibration service data corresponding to the service data label in the associated service data; determining an information matching list of four service dimension information of the calibration service data corresponding to the service data label and four service dimension information of the reference data format; performing attribute feature mapping on the tag attribute features of the service data tags according to the information matching list to obtain mapping attribute features; determining the service data to be maintained according to the mapping attribute characteristics;

determining four service dimension information of the calibration service data corresponding to the service data label in the associated service data comprises: extracting attribute features of the associated service data based on an artificial intelligence feature extraction network model to obtain tag attribute features of a service data tag; extracting attribute feature description information of tag attribute features of the service data tags, converting the attribute feature description information through a preset description information mapping matrix to obtain four target description information, and determining four information subsets with description value updating marks of the four target description information as four service dimension information of calibration service data corresponding to the service data tags;

wherein, determining the service data to be maintained according to the mapping attribute characteristics comprises: acquiring an attribute feature sequence with the maximum attribute feature value in the mapping attribute features through a preset attribute feature value identification model; determining the relative feature matching degree of the attribute feature sequence and the last attribute feature description information of the tag attribute feature of the service data tag; if the relative feature matching degree is smaller than half of the dynamic feature matching degree of the mapping attribute feature, performing recombination service data correction in a manner of performing feature recombination on the mapping attribute feature to obtain the service data to be maintained; and if the relative feature matching degree is greater than half of the dynamic feature matching degree of the mapping attribute feature, determining the mapping attribute feature as the service data to be maintained.

In a preferred embodiment, the extracting a service data cluster set of service data to be maintained based on the service data call detection result includes:

setting the data format index information in the service data to be maintained according to a data format index information preset in a reference data format;

comparing the service data calling detection result with the data format index information to determine a service data cluster set of the service data to be maintained;

comparing the service data call detection result with the data format index information to determine the service data cluster set of the service data to be maintained, wherein the step of comparing the service data call detection result with the data format index information comprises the following steps:

A second aspect of the present application provides a big data server, comprising a processing engine, a network module, and a memory; the processing engine and the memory communicate through the network module, and the processing engine reads the computer program from the memory and runs the computer program to execute the big data maintenance method applied to artificial intelligence of the first aspect.

Compared with the prior art, the big data maintenance method and the big data server applied to artificial intelligence provided by the embodiment of the application have the following technical effects: the method comprises the steps of firstly determining a service operation time interval, a service operation behavior label and service operation feedback information in service operation data of a current user service terminal, secondly determining a target service data processing record according to the service operation time interval, and then generating a service data maintenance list based on the target service data processing record and the service operation behavior label, so that data maintenance can be carried out on associated service data corresponding to the service operation data based on a service data maintenance list. Therefore, the generated service data maintenance list can indicate the big data server to perform data binding on the associated service data corresponding to the current user service terminal, so that the problem that the correctness of subsequent service processing is influenced due to confusion among the associated service data is avoided, the correct generation of a service processing result corresponding to the service operation data of the current user service terminal can be ensured, and the big data server can smoothly, accurately and coordinately deal with more and more service processing.

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 big data maintenance methods, tools, and combinations as applied to artificial intelligence as set forth in the detailed examples that follow.

Drawings

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

The big data maintenance method, system, and/or program applied to artificial intelligence in the figures will be further described in accordance with 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 exemplary big data maintenance system for artificial intelligence, according to some embodiments of the present application.

FIG. 2 is a diagram illustrating the hardware and software components of an exemplary big data server, according to some embodiments of the present application.

FIG. 3 is a flow diagram illustrating an exemplary big data maintenance method and/or process for artificial intelligence, according to some embodiments of the present application.

FIG. 4 is a block diagram illustrating an exemplary big data maintenance appliance for artificial intelligence, according to some embodiments of the present application.

Detailed Description

After finding out the problems described in the background art, the inventors have studied and analyzed the current operation conditions of a big data server and found that processing different service data in the same time period is one of the causes of service data confusion, and this time period can be understood as a service processing overlapping time period. In the service processing overlapping period, a big data server may process multiple services in parallel, which may cause a relatively weak interface shielding mechanism between different service processing threads (to avoid service data cross-talk between different services), thereby causing confusion of service data. For example, when data query (e.g., traveling route query) is performed, confusion of service data may cause a large deviation in the route query result received by the user service terminal, which may cause a subsequent adverse chain reaction.

In order to solve the above problems, the inventor innovatively provides a big data maintenance method and a big data server applied to artificial intelligence, which can maintain the service operation data and the associated service data of the current user service terminal, thereby avoiding the confusion of the service operation data and the associated service operation data of the current user service terminal with other service data in the same service processing overlapping time period in the big data server, and ensuring that the service processing result received by the current user service terminal is normal.

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application 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 application may be practiced without these specific details. In other instances, well-known big data maintenance methods, procedures, systems, compositions, and/or circuits that have been applied to artificial intelligence have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

These and other features, as well as the functions disclosed in the present application, the methods of implementing the disclosed large data maintenance techniques applied to artificial intelligence, the combination of functions and elements of related elements in structure, 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 application. 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 application. 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 application. It should be understood that the drawings are not to scale.

Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. 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 big data maintenance system 300 for artificial intelligence according to some embodiments of the present application, where the big data maintenance system 300 for artificial intelligence may include a big data server 100 and a user service terminal 200.

In some embodiments, as shown in FIG. 2, big data server 100 may include a processing engine 110, a network module 120, and a memory 130, processing engine 110 and memory 130 communicating through network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. 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 will be appreciated that the configuration shown in FIG. 2 is merely illustrative, and that the big data server 100 may also include more or fewer components than shown in FIG. 2, or have a different configuration than 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 big data maintenance method and/or process applied to artificial intelligence, which is applied to the big data server 100 in fig. 1, according to some embodiments of the present application, and may specifically include the following steps S11-S14.

It can be understood that, the big data server 100 stores in advance a service data set of each service processing overlapping time period, where the service processing overlapping time period is an overlapping time period of at least two different service processing requests, and the service data set includes service data processing records of preset service processing threads of different service processing categories corresponding to the service processing requests in the service processing overlapping time period.

For example, the traffic processing overlapping period may be understood as a period in which different traffic processing requests conflict, for example, the traffic processing period D1 of the traffic processing request Q1 is t1-t2, the traffic processing period D2 of the traffic processing request Q2 is t3-t4, and t3 is earlier than t2, so that the traffic processing overlapping period t3-t2 exists between D1 and D2. Of course, the service processing overlapping time periods may partially overlap or completely overlap, and are not limited herein.

For example, the service processing request may be initiated by the same user service terminal or may be initiated by different user service terminals. The preset service processing thread may be a network model, an algorithm, etc., and the configuration of the preset service processing thread is the prior art and is not described herein again. The service processing type is used for distinguishing different service processing requests, and the service data processing record is used for representing a processing result generated after a preset service processing thread responds to the service processing request. The service data set may include service data of different interaction scenarios and different processing periods, such as online payment data, real-time positioning data, and the like, which is not limited herein.

On the basis of the above, the big data server 100 may perform the following.

Step S11, receiving the service operation data of the current user service terminal.

For example, the service operation data may be service operation data generated when a user performs a voice operation, a touch operation, and the like through a user service terminal, where the service operation data includes a service operation period, a service operation behavior tag, and service operation feedback information of the current user service terminal. The service operation time interval refers to the time interval of service operation, the service operation behavior label is used for distinguishing different operation behaviors, and the service operation feedback information is the feedback information generated by the user service terminal according to the operation of the user.

Step S12, judging whether the service operation time interval is in the service processing overlapping time interval; and if the service operation time interval is in a service processing overlapping time interval, acquiring a target service data processing record of a preset service processing thread of each service processing request corresponding to the service operation feedback information in the service processing overlapping time interval from the service data set of the service processing overlapping time interval.

Step S13, generating a service data maintenance list corresponding to the current user service terminal according to the obtained target service data processing record corresponding to each service processing request and the service operation behavior tag.

For example, the service data maintenance list may be an indication list generated for the current user service terminal and used for preventing confusion of service data corresponding to the current user service terminal, and the service data maintenance list may indicate the big data server to perform data binding on the service data corresponding to the current user service terminal, so as to avoid that the correctness of subsequent service processing is affected by confusion among the service data.

Step S14, based on the service data maintenance list, performing data maintenance on the associated service data corresponding to the service operation data.

For example, the associated service data may be service data of multiple layers corresponding to the current user service terminal, and the associated service data may be used to perform subsequent service processing, so as to ensure that a service processing result corresponding to the service operation data of the current user service terminal is correctly generated, and further ensure that the current user service terminal can obtain a correct service processing result. Therefore, the large data server can gracefully, accurately and coordinately deal with more and more business processes.

It can be understood that based on the above steps S11-S14, firstly, the service operation time interval, the service operation behavior tag, and the service operation feedback information in the service operation data of the current user service terminal are determined, secondly, the target service data processing record is determined according to the service operation time interval, and then, the service data maintenance list is generated based on the target service data processing record and the service operation behavior tag, so that the data maintenance can be performed on the associated service data corresponding to the service operation data based on the service data maintenance list. Therefore, the generated service data maintenance list can indicate the big data server to perform data binding on the associated service data corresponding to the current user service terminal, so that the problem that the correctness of subsequent service processing is influenced due to confusion among the associated service data is avoided, the correct generation of a service processing result corresponding to the service operation data of the current user service terminal can be ensured, and the big data server can smoothly, accurately and coordinately deal with more and more service processing.

In the following, some alternative embodiments will be described, which should be understood as examples and not as technical features essential for implementing the present solution.

Before implementing the above-mentioned steps S11-S14, in order to ensure the integrity of the generated service data maintenance list, a step of determining in advance a service data set for each service processing overlapping period may be further included, which may include the following steps S21-S24.

Step S21, a first service scene data set of each service processing scene is obtained, where the first service scene data set includes service processing category information, object tag information, object interaction record information, object state information, and a corresponding service processing request of each service scene object.

For example, the service scene data set is used to distinguish different service scenes, the object tag information may be used to distinguish different user service terminals, the object interaction record information is used to record interaction behavior between different user service terminals, and the object state information is used to represent operating states of different user service terminals.

Step S22, performing service scene data screening on the first service scene data set of each service processing scene, and determining a second service scene data set of each service processing request in each service processing overlapping time period.

Step S23, for each service processing overlapping time period, respectively generating a third service scenario data set of each service processing request according to different service processing categories according to the second service scenario data set of each service processing request in the service processing overlapping time period.

Step S24, determining different set time periods according to the third service scenario data set of each service processing request, and calculating a service data processing record of each preset service processing thread corresponding to each service processing request to determine a service data set of the service processing overlapping time period.

It can be understood that based on the above steps 21 to S24, the service scene data set of each service processing scene can be analyzed, so as to calculate the service data processing record of each preset service processing thread and the service data set of the service processing overlapping time period according to the determined set time period. Therefore, the service data set can be accurately and completely generated, and the integrity of a subsequently generated service data maintenance list is ensured.

Further, the step S22 of performing traffic scenario data screening on the first traffic scenario data set of each traffic processing scenario and determining the second traffic scenario data set of each traffic processing request in each traffic processing overlapping time period may include the following steps S221 and S222.

Step S221, determining the service processing scene corresponding to each service scene object according to the object state information of each service scene object.

Step S222, performing service scene data screening on the first service scene data set of each service processing scene according to the service processing scene corresponding to each service scene object and the corresponding service processing request, and determining the second service scene data set of each service processing request in each service processing overlapping time period.

Therefore, through screening the service scene data of the first service scene data set, the second service scene data set has higher service scene timeliness compared with the first service scene data set, and the second service scene data set is matched with the service processing overlapping time period.

Further, in order to ensure timeliness of the second service scenario data set, global determination and correction of the set time period are required. To achieve this, the step of determining a different set period according to the third service scenario data set of each service processing request in step S24 may include steps S241 to S245.

Step S241, for the third service scene data set of each service processing request, calculating a real-time object interaction record of each service scene object in the third service scene data set of the service processing request in each set interaction record statistical time period.

For example, the set interaction log statistic time period may be preset, and is not limited herein.

Step S242, for each set interaction record statistic time period, determining whether the data update index of the service interaction description data recorded by the real-time object interaction of each service scene object in the set interaction record statistic time period is greater than the data update index of the preset service interaction description data.

For example, business interaction description data is used to summarize real-time object interaction records, thereby relieving data processing pressure of large data servers. The data updating index is used for representing the updating rate of the service interaction description data, and the larger the data updating index is, the faster the updating rate of the data updating index is.

In step S243, if the data update index of the service interaction description data recorded in the real-time object interaction record is greater than the data update index of the preset service interaction description data, determining the set interaction record statistical time period as a first target statistical time period.

In step S244, if the data update index of the service interaction description data recorded in the real-time object interaction record is not greater than the data update index of the preset service interaction description data, the set interaction record statistical time period is determined as a second target statistical time period.

Step S245, setting each first target statistical time period as a different set time period, correcting the plurality of second target statistical time periods according to a preset time period correction index to obtain at least one corrected time period, and setting each corrected time period as one set time period.

For example, the set period may be determined in different ways according to a first target statistical period and a second target statistical period, wherein the first target statistical period is shorter than the second target statistical period. This enables a global determination and correction of the set time period, thereby ensuring timeliness of the second set of traffic scene data.

In an actual implementation process, each service processing overlapping period includes a first service processing request and a second service processing request, where the first service processing request may be an immediate request, and the second service processing request may be a delayed request. On this basis, the step S13 of generating the content of the service data maintenance list corresponding to the current user service terminal according to the target service data processing record corresponding to each acquired service processing request and the service operation behavior tag may include the following steps S131 and S132.

Step S131, calculating a feature change trajectory of a service interaction record feature of a first object interaction record between the service operation behavior tag and a target service data processing record corresponding to the first service processing request, and a feature change trajectory of a service interaction record feature of a second object interaction record between target service data processing records corresponding to the second service processing request.

For example, the service interaction record features are used for describing the object interaction records from different dimensions, the service interaction record features of different object interaction records have differences, the feature change track may be a track for recording changes of the service interaction record features according to a time sequence, and the track may be a track curve or other graphical forms, which is not limited herein.

Step S132, determining a service data maintenance list corresponding to the current user service terminal according to the feature change trajectory of the service interaction record feature of the first object interaction record and the feature change trajectory of the service interaction record feature of the second object interaction record.

In this way, by applying the above steps S131 and S132, the instant request and the delayed request can be taken into account, and the characteristic change tracks of different requests can be further taken into account, so that the global timeliness of the service data maintenance list can be ensured, and the maintenance for the stale failure service data that is recorded too much in the service data maintenance list is avoided, thereby reducing the operating pressure of the big data server and improving the efficiency of the big data server in data maintenance.

On the basis of the above, the determining, by the step S132, the content of the service data maintenance list corresponding to the current user service terminal according to the feature change trajectory of the service interaction record feature of the first object interaction record and the feature change trajectory of the service interaction record feature of the second object interaction record may include the following steps S1321 to S1323.

Step S1321, determining whether the track change index weight of the feature change track of the service interaction record feature of the first object interaction record is greater than the track change index weight of the feature change track of the service interaction record feature of the second object interaction record.

For example, the track change index weight can be used for representing the stability of the characteristic change track, the value of the track change index weight can be 0-1, and the higher the value is, the more stable the characteristic change track is.

Step S1322 is to determine, according to the second service processing request, a service data maintenance list corresponding to the current user service terminal if the track change index weight of the feature change track of the service interaction record feature recorded by the first object interaction is greater than the track change index weight of the feature change track of the service interaction record feature recorded by the second object interaction.

Step S1323, if the track change index weight of the feature change track of the service interaction record feature of the first object interaction record is less than or equal to the track change index weight of the feature change track of the service interaction record feature of the second object interaction record, determining a service data maintenance list corresponding to the current user service terminal according to the first service processing request.

It can be understood that by implementing the above steps S1321 to S1323, the service data maintenance list can be determined by using different service processing requests based on the track change index weight, so that the data to be maintained recorded in the service data maintenance list can be ensured to be stable for a long time, and other adverse effects caused by unstable data when data maintenance is subsequently performed can be avoided.

In another embodiment, the big data server stores a plurality of service evaluation coefficients corresponding to each service processing overlapping period and an index weight interval of service interaction record characteristics of an object interaction record corresponding to each service evaluation coefficient, the service evaluation coefficients are used for representing the reliability of service processing, a higher service evaluation coefficient indicates that the reliability of service processing is higher, the probability of service processing error reporting is lower, and the index weight interval may be a numerical value interval. Based on this, the step S132 may determine the service data maintenance list corresponding to the current user service terminal according to the feature change trajectory of the service interaction record feature of the first object interaction record and the feature change trajectory of the service interaction record feature of the second object interaction record, and may further include the following steps S132 a-S132 e.

Step S132a, find a first service evaluation coefficient corresponding to the index weight interval of the service interaction record feature of the first object interaction record corresponding to the feature change trajectory of the service interaction record feature of the first object interaction record and a second service evaluation coefficient corresponding to the index weight interval of the service interaction record feature of the second object interaction record corresponding to the feature change trajectory of the service interaction record feature of the second object interaction record.

Step S132b, determining whether the first traffic evaluation coefficient and the second traffic evaluation coefficient are greater than a set traffic evaluation coefficient, respectively.

Step S132c, if the first service evaluation coefficient is greater than the set service evaluation coefficient, determining a service data maintenance list corresponding to the current user service terminal according to the first service processing request.

Step S132d, if the second service evaluation coefficient is greater than the set service evaluation coefficient, determining a service data maintenance list corresponding to the current user service terminal according to the second service processing request.

Step S132e, if the first service evaluation coefficient and the second service evaluation coefficient are not greater than the set service evaluation coefficient, iteratively updating the service data set of each service processing overlapping time period, and returning to the step of calculating a feature change trajectory of a service interaction record feature of a first object interaction record between the service operation behavior tag and the target service data processing record corresponding to the first service processing request and a feature change trajectory of a service interaction record feature of a second object interaction record between the target service data processing records corresponding to the second service processing request.

For example, the iterative update may be a reconstruction or modification of the traffic data set.

It can be understood that by implementing the above steps S132 a-S132 e, the service data maintenance list can be determined from different angles, so as to improve the flexibility of generating the service data maintenance list, so that the scheme can be applied to different service environments, and avoid the generation of the service data maintenance list from being affected due to a problem occurring in a certain service environment.

On the basis of the above-mentioned step S132 a-step S132e, the method further includes a step of presetting a plurality of service evaluation coefficients corresponding to each service processing overlapping time period and an index weight interval of the service interaction record characteristic of the object interaction record corresponding to each service evaluation coefficient, and the step may include the following steps (1) -step (3).

(1) And calculating the service interaction record characteristics of the object interaction records between the target service data processing records corresponding to the first service processing request and the target service data processing records corresponding to the second service processing request in the service processing overlapping time period aiming at each service processing overlapping time period.

(2) And determining a plurality of service evaluation coefficients and a service evaluation time sequence weight corresponding to each service evaluation coefficient according to the service data set of the service processing overlapped time period.

For example, the traffic evaluation timing weight is used for characterizing the timeliness of the traffic evaluation coefficient.

(3) And calculating a characteristic weighting result between the service evaluation time sequence weight corresponding to each service evaluation coefficient and the service interaction record characteristic of the object interaction record, and configuring an index weight interval of the service interaction record characteristic of the object interaction record corresponding to each service evaluation coefficient according to the characteristic weighting result between the service evaluation time sequence weight corresponding to each service evaluation coefficient and the service interaction record characteristic of the object interaction record.

By applying the steps (1) to (3), the correlation index weight section can be determined in advance, and the accuracy and reliability of the subsequent data processing can be ensured.

In addition, after the service data maintenance list corresponding to the current user service terminal is generated, the service data maintenance list can be sent to the associated user service terminal of the current user service terminal, so that the associated user service terminal can perform corresponding service data adjustment according to the service data maintenance list, and the subsequent service interaction efficiency is improved.

In some examples, in order to implement comprehensive maintenance on the associated service data and ensure that the service data which is not confused is not used in subsequent service processing, the data maintenance on the associated service data corresponding to the service operation data based on the service data maintenance list described in step S14 may include the following steps S141 to S144.

Step S141, based on the service data maintenance list, performing service data correction on the acquired associated service data to obtain service data to be maintained, which is matched with a pre-stored reference data format.

Step S142, determining a service data call detection result of the service data to be maintained by performing service data call detection on the service data to be maintained.

And step S143, extracting a service data cluster set of the service data to be maintained based on the service data calling detection result.

And step S144, performing service data deletion, service data reconstruction and service data expansion on the service data to be maintained according to the service data clustering set.

By adopting the design, based on the steps S141 to S144, service data deletion, service data reconstruction, and service data expansion can be performed on service data to be maintained, so that all-around maintenance on associated service data is realized, and it is ensured that confused service data is not used in subsequent service processing.

Further, the performing of the service data correction on the acquired associated service data to obtain the service data to be maintained, which is matched with the pre-stored reference data format, as described in step S141, may include the following steps S1411 to S1413.

Step S1411, determine four service dimension information of the calibration service data corresponding to the service data tag in the associated service data.

For example, the service dimension information may be a service type, a service requirement, a service object, and a service period, and is not limited to four service dimension information in actual implementation.

Step S1412, determining an information matching list of the four service dimension information of the calibration service data corresponding to the service data label and the four service dimension information of the reference data format.

Step S1413, performing attribute feature mapping on the tag attribute feature of the service data tag according to the information matching list to obtain a mapping attribute feature; and determining the service data to be maintained according to the mapping attribute characteristics.

Therefore, the compatibility of the data format can be ensured by determining the service data to be maintained, so that the efficiency of subsequent data maintenance is improved, and errors are avoided.

Further, the determining the four service dimension information of the calibration service data corresponding to the service data tag in the associated service data described in step S1411 includes: extracting attribute features of the associated service data based on an artificial intelligence feature extraction network model to obtain tag attribute features of a service data tag; extracting attribute feature description information of the label attribute features of the service data labels, converting the attribute feature description information through a preset description information mapping matrix to obtain four target description information, and determining four information subsets with description value updating marks of the four target description information as four service dimension information of the calibration service data corresponding to the service data labels.

Further, the determining the service data to be maintained according to the mapping attribute described in step S1413 includes: acquiring an attribute feature sequence with the maximum attribute feature value in the mapping attribute features through a preset attribute feature value identification model; determining the relative feature matching degree of the attribute feature sequence and the last attribute feature description information of the tag attribute feature of the service data tag; if the relative feature matching degree is smaller than half of the dynamic feature matching degree of the mapping attribute feature, performing recombination service data correction in a manner of performing feature recombination on the mapping attribute feature to obtain the service data to be maintained; and if the relative feature matching degree is greater than half of the dynamic feature matching degree of the mapping attribute feature, determining the mapping attribute feature as the service data to be maintained.

Further, the step S143 of extracting the service data cluster set of the service data to be maintained based on the service data call detection result includes the following steps S1431 and S1432.

Step S1431, setting the data format index information in the service data to be maintained according to a data format index information preset in a reference data format.

Step S1432, comparing the service data call detection result with the data format index information to determine a service data cluster set of the service data to be maintained.

By the design, based on the step S1431 and the step S1432, the service data cluster set of the service data to be maintained can be accurately determined, so that an execution basis is provided for subsequent data maintenance.

Further, comparing the service data call detection result with the data format index information to determine the service data cluster set of the service data to be maintained, which is described in step S1432, may be implemented by the following contents described in steps S14321 to S14325.

Step S14321, determining a first data format comparison result of current data format index information, first data format index information and fifth data format index information of a data calling detection record of the service data calling detection result, and determining a service data behavior element in a service data clustering set of the service data to be maintained according to the first data format comparison result; the first data format index information is target description information between a business data behavior element and a business data type element and corresponding to last attribute feature description information of a label attribute feature of a business data label, and the fifth data format index information is target description information between the business data type element and a business data object element and corresponding to the first data format index information.

Step S14322, determining a second data format comparison result of the current data format index information of the data call detection record of the service data call detection result and the first data format index information, the second data format index information and the fifth data format index information, and determining a service data type element and a service data object element in the service data cluster set of the service data to be maintained according to the second data format comparison result; and the second data format index information is target description information between the service data type element and the service data demand element and corresponding to the last attribute feature description information of the tag attribute feature of the service data tag.

Step S14323, determining a third data format comparison result between the current data format index information of the data call detection record of the service data call detection result and the second data format index information, the third data format index information, and the fifth data format index information, and determining a service data requirement element in the service data cluster set of the service data to be maintained according to the third data format comparison result; and the third data format index information is target description information between the service data demand element and the service data associated element and corresponding to the last attribute feature description information of the tag attribute feature of the service data tag.

Step S14324, determining a fourth data format comparison result between the current data format index information recorded in the data call detection of the service data call detection result and the third data format index information, the fourth data format index information, and the fifth data format index information, and determining a service data related element in the service data cluster set of the service data to be maintained according to the fourth data format comparison result.

Step S14325, determining a fifth data format comparison result of the current data format index information of the data call detection record of the service data call detection result, the fourth data format index information and the fifth data format index information, and determining a service data label sequence element in the service data clustering set of the service data to be maintained according to the fifth data format comparison result; the fourth data format index information is target description information between the service data associated element and the service data label sequence element and corresponding to the last attribute feature description information of the label attribute feature of the service data label.

It can be understood that by implementing the above steps S14321 to S14325, different elements of the service data cluster set of the service data to be maintained can be determined, and these elements can be used as important bases for performing subsequent service data deletion, service data reconstruction and service data expansion, thereby ensuring the accuracy of subsequent data maintenance.

In an alternative embodiment, the data maintenance of the associated service data corresponding to the service operation data based on the service data maintenance list as described in step S14 further includes integrating data call paths of the associated service data. For example, the call path integration may be performed on the associated service data corresponding to the service operation data according to a preset manner. Therefore, when the service processing corresponding to the service operation data is executed, the data can be called only through the data calling path, so that other service data can be prevented from being called, and further confusion is avoided.

Fig. 4 is a block diagram of an exemplary big data maintenance apparatus 140 for artificial intelligence, according to some embodiments of the present application, where the big data maintenance apparatus 140 for artificial intelligence is applied to a big data server, a service data set of each service processing overlapping time interval is stored in the big data server, the service processing overlapping time interval is an overlapping time interval of at least two different service processing requests, the service data set includes service data processing records of preset service processing threads corresponding to different service processing categories for each service processing request in the service processing overlapping time interval, and the big data maintenance apparatus 140 for artificial intelligence may include the following functional modules.

The operation data receiving module 141 is configured to receive service operation data of a current user service terminal, where the service operation data includes a service operation time period, a service operation behavior tag, and service operation feedback information of the current user service terminal.

A processing record obtaining module 142, configured to determine whether the service operation time period is in a service processing overlapping time period; and if the service operation time interval is in a service processing overlapping time interval, acquiring a target service data processing record of a preset service processing thread of each service processing request corresponding to the service operation feedback information in the service processing overlapping time interval from the service data set of the service processing overlapping time interval.

And a maintenance list generating module 143, configured to generate a service data maintenance list corresponding to the current user service terminal according to the obtained target service data processing record corresponding to each service processing request and the service operation behavior tag.

And a service data maintenance module 144, configured to perform data maintenance on the associated service data corresponding to the service operation data based on the service data maintenance list.

Reference may be made to the description of the above method embodiments with respect to the description of the above apparatus embodiments.

Based on the same inventive concept, the big data maintenance system applied to artificial intelligence is also provided, and the description about the system is as follows.

A big data maintenance system applied to artificial intelligence comprises a big data server and a user service terminal which are communicated with each other, wherein a service data set of each service processing overlapping time interval is stored in the big data server, the service processing overlapping time interval is the overlapping time interval of at least two different service processing requests, the service data set comprises service data processing records of each preset service processing thread corresponding to each service processing request in different service processing categories in the service processing overlapping time interval, and the big data server is used for:

and performing data maintenance on the associated service data corresponding to the service operation data based on the service data maintenance list.

Reference may be made to the description of the above method embodiments with respect to the description of the above system embodiments.

It should be understood that, for technical terms that are not noun-explained in the above, a person skilled in the art can deduce and unambiguously determine the meaning of the present invention from the above disclosure, for example, for some values, coefficients, weights, indexes, factors and other terms, a person skilled in the art can deduce and determine from the logical relationship between the above and the below, and the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, and for example, 50 to 100, which is not limited herein.

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 above disclosure of the embodiments of the present application will be apparent to those skilled in the art from the above disclosure. 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 application, 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 considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification 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 present application is included in at least one embodiment of the present application. 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 features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.

In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application 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 application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.

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 application 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).

Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. 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 present application, 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 require 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.

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