CN111984744A

CN111984744A - Information processing method based on remote communication and artificial intelligence and cloud service platform

Info

Publication number: CN111984744A
Application number: CN202010813843.9A
Authority: CN
Inventors: 孙小丽
Original assignee: Individual
Current assignee: Beijing Momo Information Technology Co Ltd
Priority date: 2020-08-13
Filing date: 2020-08-13
Publication date: 2020-11-24
Anticipated expiration: 2040-08-13
Also published as: CN111984744B; CN113076381A

Abstract

The embodiment of the application provides an information processing method based on remote communication and artificial intelligence and a cloud service platform, after an optimized communication interaction object corresponding to service access big data uploaded by an online communication service terminal is obtained, communication optimization entity information between a communication optimization signature project corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts is obtained, then the communication optimization entity information is processed according to a preset artificial intelligence model, a corresponding target communication optimization strategy is generated, and the target communication optimization strategy is sent to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter updating content corresponding to each communication optimization element appointed in the target communication optimization strategy based on the target communication optimization strategy. Thus, communication optimization can be performed on the instant communication interaction object aiming at optimization.

Description

Information processing method based on remote communication and artificial intelligence and cloud service platform

Technical Field

The application relates to the technical field of intelligent online communication and big data, in particular to an information processing method and a cloud service platform based on remote communication and artificial intelligence.

Background

At present, the intelligent online communication technology brings great convenience to internet services (such as live e-commerce services, telemedicine services, remote intelligent online monitoring services, online education services, online office services, and the like), so that for an online communication service terminal, stability in a communication process is very important. If the communication optimization is not performed on the optimized communication interactive object in time, the stability of the online communication service is greatly influenced.

Disclosure of Invention

In view of this, an object of the present application is to provide an information processing method and a cloud service platform based on remote communication and artificial intelligence, which can perform communication optimization for an optimized communication interaction object in time.

According to a first aspect of the present application, there is provided an information processing method based on remote communication and artificial intelligence, applied to a cloud service platform in communication connection with a plurality of online communication service terminals, the method including:

acquiring an optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal;

acquiring communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, wherein the communication optimization entity information comprises communication optimization entities corresponding to the communication optimization signature items and the preset communication optimization scripts and entity optimization configuration information of the communication optimization entities;

processing communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts according to a preset artificial intelligence model, generating a corresponding target communication optimization strategy, and sending the target communication optimization strategy to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element appointed in the target communication optimization strategy based on the target communication optimization strategy.

In a possible implementation manner of the first aspect, the step of obtaining an optimized communication interaction object corresponding to service access big data uploaded by the online communication service terminal includes:

acquiring a service access big data set from online communication service terminals subscribing different internet information services, and acquiring an online communication fault data set according to the service access big data set, wherein the service access big data set comprises a continuous preset number of service access big data, and the online communication fault data set comprises a continuous preset number of online communication fault data;

based on the service access big data set, acquiring a service access interaction feature set through a first service feature extraction unit included in a service relationship identification model, wherein the service access interaction feature set comprises a preset number of service access interaction features;

acquiring an online communication fault interaction feature set through a second service feature extraction unit included in the service relation identification model based on the online communication fault data set, wherein the online communication fault interaction feature set comprises a preset number of online communication fault interaction features;

based on the service access interaction feature set and the online communication fault interaction feature set, acquiring an optimized communication interaction object corresponding to the service access big data through an information classification unit included in the service relationship identification model, and taking the optimized communication interaction object as the optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal.

In a possible implementation manner of the first aspect, the step of obtaining, by an information classification unit included in the business relationship identification model, an optimized communication interaction object corresponding to the service access big data based on the service access interaction feature set and the online communication fault interaction feature set includes:

acquiring first process node matching information through a process node matching layer included in a first business authentication process unit aiming at each service access interaction feature in the service access interaction feature set, wherein the first business authentication process unit belongs to the business relation identification model;

acquiring first process permission characteristic information through a process permission matching layer included by the first service authentication process unit aiming at each service access interaction characteristic in the service access interaction characteristic set;

acquiring first authentication relationship information through an authentication relationship layer included in the first service authentication process unit based on the first process node matching information and the first process authority feature information for each service access interaction feature in the service access interaction feature set;

for each service access interaction feature in the service access interaction feature set, based on the first authentication relationship information and the service access interaction feature, obtaining first communication authentication information through a first process permission matching layer included in the first service authentication process unit, wherein each first communication authentication information corresponds to one service access interaction feature;

based on the online communication fault interaction feature set, acquiring second process node matching information through a process node matching layer included in a second service authentication process unit aiming at each online communication fault interaction feature in the online communication fault interaction feature set, wherein the second service authentication process unit belongs to the service relation identification model;

acquiring second process permission characteristic information through a process permission matching layer included by the second service authentication process unit aiming at each online communication fault interaction characteristic in the online communication fault interaction characteristic set;

acquiring second authentication relationship information through an authentication relationship layer included in the second service authentication process unit based on the second process node matching information and the second process authority characteristic information for each online communication fault interaction characteristic in the online communication fault interaction characteristic set;

acquiring second communication authentication information through a second process permission matching layer included in the second service authentication process unit aiming at each online communication fault interactive feature in the online communication fault interactive feature set based on the second authentication relationship information and the online communication fault interactive feature, wherein each piece of second communication authentication information corresponds to one online communication fault interactive feature;

matching a preset number of first communication authentication information and a preset number of second communication authentication information to obtain a preset number of matched target communication authentication information, wherein each target communication authentication information comprises a first communication authentication information and a second communication authentication information;

based on the preset number of target communication authentication information, acquiring a preset number of first sub-communication authentication information through a first authentication positioning layer included in an authentication positioning unit, wherein the authentication positioning unit belongs to the service relationship identification model;

based on the preset number of first sub-communication authentication information, acquiring a preset number of second sub-communication authentication information through a second authentication positioning layer included in the authentication positioning unit;

determining a preset number of authentication nodes according to the preset number of second sub-communication authentication information, wherein each authentication node corresponds to one target communication authentication information;

determining authentication relation communication triggering information according to the preset number of target communication authentication information and the preset number of authentication nodes;

and based on the authentication relationship communication trigger information, acquiring an optimized communication interaction object corresponding to the service access big data set through the information classification unit included in the service relationship identification model.

In a possible implementation manner of the first aspect, the step of processing, according to a preset artificial intelligence model, communication optimization entity information between a communication optimization signature item corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts to generate a corresponding target communication optimization policy includes:

determining an optimization importance degree between each communication optimization signature item and each preset communication optimization script based on the communication optimization entity information, obtaining a communication optimization feature vector of each communication optimization signature item and a communication optimization feature vector of each preset communication optimization script based on the optimization importance degree, determining a similar communication optimization signature item of each communication optimization signature item according to a first preset artificial intelligence model based on the communication optimization feature vector of each communication optimization signature item, and determining a similar communication optimization strategy corresponding to each communication optimization signature item according to a second preset artificial intelligence model based on the communication optimization feature vector of each preset communication optimization script;

the target communication optimization strategy comprises a preset communication optimization script of a communication optimization entity corresponding to each communication optimization signature item, a preset communication optimization script of a communication optimization entity corresponding to the similar communication optimization signature item and the similar communication optimization strategy of each communication optimization signature item;

the preset artificial intelligence model comprises a first preset artificial intelligence model and a second preset artificial intelligence model, the first preset artificial intelligence model is obtained based on the training of communication optimization signature project labels corresponding to first training samples and each first training sample, and the second preset artificial intelligence model is obtained based on the training of communication optimization strategy labels corresponding to second training samples and each first second sample.

In a possible implementation manner of the first aspect, before the step of determining an optimization importance degree between each communication optimization signature item and each preset communication optimization script based on the communication optimization entity information, the method further includes:

deleting a to-be-processed communication optimization entity in the communication optimization entities based on the entity optimization configuration information to obtain processed communication optimization entity information, wherein the to-be-processed communication optimization entity specifies a communication optimization entity of which the entity optimization configuration information in the communication optimization entity of a communication optimization strategy does not meet specified service conditions, and the specified communication optimization strategy is a preset communication optimization script of which the corresponding communication optimization signature item quantity is greater than a communication optimization signature item threshold value in the preset communication optimization scripts;

the determining the optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively based on the communication optimization entity information comprises:

determining the optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively based on the processed communication optimization entity information;

the appointed service condition comprises that entity optimization configuration information is in a first appointed service interval starting from an appointed service node, and an ending service node of the first appointed service interval is a current service node.

In a possible implementation manner of the first aspect, before the step of obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, the method further includes:

the method comprises the steps that a communication optimization script to be processed in a plurality of communication optimization scripts is obtained, and the communication optimization script to be processed is not matched with a corresponding communication optimization entity in a second specified service interval;

and deleting the communication optimization script to be processed in the communication optimization scripts to obtain the preset communication optimization scripts.

In a possible implementation manner of the first aspect, the determining, based on the communication optimization entity information, an optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively includes:

acquiring the occupation proportion of each communication optimization entity between a communication optimization signature project which is currently subjected to optimization importance degree calculation and a preset communication optimization script which is currently subjected to optimization importance degree calculation;

and adding the occupation proportion of each communication optimization entity to obtain the optimization importance degree between the communication optimization signature item which is currently subjected to optimization importance degree calculation and the preset communication optimization script which is currently subjected to optimization importance degree calculation.

In a possible implementation manner of the first aspect, the obtaining, based on the optimization importance degree, a communication optimization feature vector of each communication optimization signature item and a communication optimization feature vector of each preset communication optimization script includes:

acquiring an initial communication optimization characteristic vector of each communication optimization signature project and an initial communication optimization characteristic vector of each preset communication optimization script;

acquiring the matching confidence of any communication optimization signature item and any communication optimization strategy based on the initial communication optimization feature vector of any communication optimization signature item in the communication optimization signature items and any communication optimization script in the preset communication optimization scripts in sequence to obtain global matching confidence distribution;

acquiring the optimized node distribution of any communication optimized signature item and any communication optimized strategy based on the optimized importance degree of any communication optimized signature item in the communication optimized signature items and any communication optimized strategy in the preset communication optimized scripts and a first optimized node distribution evaluation rule in sequence to obtain a first global optimized node distribution;

acquiring the difference between the global matching confidence coefficient distribution and the first global optimization node distribution based on a first distribution difference evaluation function, and updating the initial communication optimization feature vector based on the difference to obtain a first communication optimization feature vector to be fused;

acquiring the matching degree of any communication optimization signature item and any communication optimization strategy based on the initial communication optimization feature vector of any communication optimization signature item in the communication optimization signature items and any communication optimization strategy in the preset communication optimization scripts in sequence to obtain global matching degree distribution;

acquiring the optimized node distribution of any communication optimized signature item and any communication optimized strategy based on the optimized importance degree of any communication optimized signature item in the communication optimized signature items and any communication optimized strategy in the preset communication optimized scripts and a second optimized node distribution evaluation rule in sequence to obtain a second global optimized node distribution;

obtaining the difference between the global matching degree distribution and the second global optimization node distribution based on a second distribution difference evaluation function, and updating the initial communication optimization feature vector based on the difference to obtain a second communication optimization feature vector to be fused;

and fusing the first communication optimization feature vector to be fused with the second communication optimization feature vector to be fused to obtain each communication optimization signature item and each communication optimization feature vector corresponding to each preset communication optimization script.

In a possible implementation manner of the first aspect, the step of obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts includes:

respectively obtaining a communication optimization entity of a communication optimization signature item corresponding to each optimized communication interaction object from the preset communication optimization scripts; and

respectively obtaining communication optimization entity information pre-configured in the preset communication optimization script by each communication optimization entity from the plurality of preset communication optimization scripts;

each preset communication optimization script comprises communication optimization entities of different communication optimization signature items and communication optimization entity information configured in advance by the communication optimization entities.

According to a second aspect of the present application, there is provided an information processing apparatus based on remote communication and artificial intelligence, applied to a cloud service platform in communication connection with an online communication terminal, the apparatus including:

the first acquisition module is used for acquiring an optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal;

the second obtaining module is used for obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, wherein the communication optimization entity information comprises communication optimization entities corresponding to the communication optimization signature items and the preset communication optimization scripts and entity optimization configuration information of the communication optimization entities;

and the generating module is used for processing communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts according to a preset artificial intelligence model, generating a corresponding target communication optimization strategy, and sending the target communication optimization strategy to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element appointed in the target communication optimization strategy based on the target communication optimization strategy.

In a third aspect, an embodiment of the present invention further provides an information processing system based on remote communication and artificial intelligence, where the information processing system based on remote communication and artificial intelligence includes a cloud service platform and an online communication terminal in communication connection with the cloud service platform;

the cloud service platform is used for acquiring an optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal;

the cloud service platform is used for acquiring communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, and the communication optimization entity information comprises communication optimization entities corresponding to the communication optimization signature items and the preset communication optimization scripts and entity optimization configuration information of the communication optimization entities;

the cloud service platform is used for processing communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts according to a preset artificial intelligence model, generating corresponding target communication optimization strategies, and sending the target communication optimization strategies to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element appointed in the target communication optimization strategies based on the target communication optimization strategies.

In a fourth aspect, an embodiment of the present invention further provides a cloud service platform, where the cloud service platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is used for being communicatively connected to at least one online communication terminal, the machine-readable storage medium is used for storing a program, an instruction, or a code, and the processor is used for executing the program, the instruction, or the code in the machine-readable storage medium to execute the information processing method based on remote communication and artificial intelligence in the first aspect or any one of possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform an information processing method based on remote communication and artificial intelligence in the first aspect or any one of the possible designs of the first aspect.

Based on any aspect, after obtaining the optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal, the method obtains communication optimization entity information between communication optimization signature items corresponding to the optimized communication interaction objects and a plurality of preset communication optimization scripts, processes the communication optimization entity information according to a preset artificial intelligence model to generate a corresponding target communication optimization strategy, and sends the target communication optimization strategy to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element appointed in the target communication optimization strategy based on the target communication optimization strategy. Thus, communication optimization can be performed on the instant communication interaction object aiming at optimization.

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.

FIG. 1 is a schematic diagram illustrating an application scenario of an information processing system based on remote communication and artificial intelligence provided by an embodiment of the present application;

FIG. 2 is a flow chart illustrating an information processing method based on remote communication and artificial intelligence provided by an embodiment of the application;

FIG. 3 is a schematic diagram illustrating functional modules of an information processing apparatus based on remote communication and artificial intelligence provided by an embodiment of the present application;

fig. 4 is a schematic component structural diagram of a cloud service platform for executing the above-mentioned information processing method based on remote communication and artificial intelligence according to an embodiment of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

FIG. 1 is an interactive schematic diagram of an information handling system 10 based on telecommunications and artificial intelligence provided in accordance with an embodiment of the present invention. The telematics system 10 based on remote communication and artificial intelligence can include a cloud service platform 100 and an online communication terminal 200 communicatively connected to the cloud service platform 100. The telematics-based information handling system 10 shown in FIG. 1 is but one possible example, and in other possible embodiments, the telematics-based information handling system 10 may include only some of the components shown in FIG. 1 or may include additional components.

In this embodiment, the cloud service platform 100 and the online communication terminal 200 in the information processing system 10 based on remote communication and artificial intelligence may cooperatively perform the information processing method based on remote communication and artificial intelligence described in the following method embodiment, and specific steps of the cloud service platform 100 and the online communication terminal 200 may refer to the detailed description of the following method embodiment.

Based on the inventive concept of the technical scheme provided by the application, the cloud service platform 100 provided by the application can be applied to scenes such as smart medical care, smart city management, smart industrial internet, general service monitoring management and the like, which can apply a big data technology or a cloud computing technology, and the like, and can also be applied to scenes such as but not limited to new energy automobile system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud automobile management platform, block chain financial data service platform and the like, but not limited to these.

To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of an information processing method based on remote communication and artificial intelligence according to an embodiment of the present invention, where the information processing method based on remote communication and artificial intelligence according to the embodiment may be executed by the cloud service platform 100 shown in fig. 1, and the information processing method based on remote communication and artificial intelligence is described in detail below.

And step S110, acquiring an optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal.

Step S120, communication optimization entity information between communication optimization signature items corresponding to the optimized communication interaction objects and the preset communication optimization scripts is obtained.

And step S130, processing communication optimization entity information between the communication optimization signature items corresponding to the multiple optimized communication interaction objects and the multiple preset communication optimization scripts according to a preset artificial intelligence model, generating corresponding target communication optimization strategies, and sending the target communication optimization strategies to the online communication service terminal.

In this embodiment, the communication optimization entity information may specifically include communication optimization entities corresponding to the communication optimization signature items and the preset communication optimization scripts, and entity optimization configuration information of the communication optimization entities. It should be noted that the optimized communication interaction object may be understood as a communication interaction object specified to be optimized, and may generally refer to a protocol object of a data transmission protocol, a call object of a data call service, and the like, and the corresponding communication optimized signature item may be understood as a signature item mapped by the communication interaction object in an instantiation process, and may be used to represent a matching identification tag of the communication interaction object. The preset communication optimization script can be configured in advance, and the communication optimization entity can be understood as an optimizable unit specifically corresponding to the communication interaction object, for example, a protocol object of a data transmission protocol, and an optimizable part in a call object of a data call service. The entity optimization configuration information may be understood as specific optimization configuration information of the above optimizable part, and may be configured in advance, and details are not particularly limited.

In this embodiment, the online communication service terminal may perform, on the basis of the target communication optimization policy, communication optimization of parameter update contents corresponding to each communication optimization element specified in the target communication optimization policy. For example, assuming that each communication optimization element specified by the target communication optimization policy is a communication optimization element a, a communication optimization element B, and a communication optimization element C, respectively, the online communication service terminal performs communication optimization of the parameter update contents corresponding to the communication optimization element a, the communication optimization element B, and the communication optimization element C.

Based on the above steps, in this embodiment, after obtaining an optimized communication interaction object corresponding to service access big data uploaded by the online communication service terminal, communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts is obtained, then the communication optimization entity information is processed according to a preset artificial intelligence model to generate a corresponding target communication optimization policy, and the target communication optimization policy is sent to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element specified in the target communication optimization policy on each communication optimization element based on the target communication optimization policy. Thus, communication optimization can be performed on the instant communication interaction object aiming at optimization.

In a possible implementation manner, for step S110, in the process of obtaining the optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal, the following exemplary sub-steps may be implemented.

The substep S111 acquires a service access big data set from the online communication service terminal 200 subscribing different internet information services, and acquires an online communication failure data set according to the service access big data set.

And a substep S112, based on the service access big data set, obtaining a service access interaction feature set through a first service feature extraction unit included in the service relationship identification model.

And a substep S113, based on the online communication fault data set, acquiring an online communication fault interaction feature set through a second service feature extraction unit included in the service relationship identification model.

And a substep S114, based on the service access interaction feature set and the online communication fault interaction feature set, obtaining an optimized communication interaction object corresponding to the service access big data through an information classification unit included in the service relationship identification model, and determining an information generation result of the service access big data set according to the optimized communication interaction object.

In this embodiment, the service access big data set may specifically include a preset number of continuous service access big data, and the online communication failure data set may specifically include a preset number of continuous online communication failure data. The inventor of the application finds that online communication fault interaction behavior is characterized in that online communication fault data are fused into a data recording area corresponding to the original service access big data and the associated service access big data, and therefore based on the design, subsequent communication optimization operation can be conveniently carried out by combining the characteristic information.

In this embodiment, the service access big data may be understood as big data information generated during the service access initiated by the online communication service terminal 200, including but not limited to user operation information, background push information, and the like, the online communication failure data may be understood as upload report information related to a node corresponding to the online communication failure behavior during the service access initiated by the online communication service terminal 200 when the online communication failure behavior exists, the upload report information may be configured with a corresponding template in advance at the online communication service terminal 200, for example, during the service access initiated for the service a, the online communication failure behavior exists during the node a1, the upload report information sent at this time may include data transmission record information corresponding to the service a, the node a1 process, and the node a1 process, and the data transmission record information may include but not limited to data transmission protocol information, and the like, Data call service information, and specifically transmitted data content fields, etc.

In this embodiment, the service access interaction feature set includes a preset number of service access interaction features, the online communication fault interaction feature set includes a preset number of online communication fault interaction features, and a specific feature extraction manner will be exemplarily described in detail later.

In this embodiment, the optimized communication interaction object corresponding to the service access big data is obtained through the information classification unit included in the service relationship identification model, and an information generation result of the service access big data set may be determined according to the optimized communication interaction object, where the information generation result may specifically include the optimized communication interaction object and an optimizable part of the optimized communication interaction object.

Based on the above steps, in this embodiment, it is considered that, because the online communication fault interaction behavior is characterized in that online communication fault data is fused to a data recording region corresponding to the original service access big data and the associated service access big data, some specific data recording regions inconsistent with the original access data are generated in the data recording region corresponding to the original service access big data by the online communication fault data, and based on this, feature information of the service access big data set and feature information of the online communication fault data set are concerned at the same time, an optimized communication interaction object corresponding to the service access big data set can be effectively discovered, so as to perform communication optimization for the optimized communication interaction object in time in the following process.

In one possible implementation, step S111 may be implemented, for example, by the following exemplary substeps, which are described in detail below.

And a substep S1111, for each service access big data in the service access big data set, obtaining a corresponding online communication fault uploading table from a data recording area corresponding to the service access big data.

In this embodiment, the online communication failure upload table may specifically include an online communication failure report of each service access node corresponding to the service access big data.

And a substep S1112, generating online communication fault data corresponding to each service access big data according to the online communication fault uploading table corresponding to each service access big data.

In one possible implementation, step S1120 may be implemented by the following exemplary substeps, which are described in detail below.

And a substep S1121, determining at least one operation request interaction element of the service access big data set through a first service feature extraction unit included in the service relationship identification model, and determining an interaction service label of each operation request interaction element.

And a sub-step S1122 of determining the matching importance weight of the operation request interaction element according to the interaction service label.

And a substep S1123 of obtaining a first matching residual queue of a single service interaction matching process corresponding to the service relationship identification model and a second matching residual queue of a corresponding global service interaction matching process, wherein the matching sequence of the single service interaction matching process to the operation request interaction elements is prior to the global service interaction matching process.

And a substep S1124, calculating a ratio between the first matching remaining queue and the second matching remaining queue to obtain a target queue comparison parameter between the first matching remaining queue and the second matching remaining queue.

In the sub-step S1125, when the target queue comparison parameter is greater than the preset fixed ratio, the fixed ratio range greater than the preset fixed ratio is divided into a first set similarity range and a second fixed ratio range, and the second fixed ratio range is greater than the first set similarity range.

In sub-step S1126, if the fixed ratio range of the target queue comparison parameter is the first set similarity range, it is determined that the matching importance weight range that needs to be processed by the single service interaction matching process includes the first matching importance weight and the second matching importance weight.

In the substep S1127, if the fixed ratio range of the target queue comparison parameter is the second fixed ratio range, it is determined that the matching importance weight range to be processed by the single service interaction matching process includes the first matching importance weight.

And a substep S1128 of allocating the operation request interaction elements with the matching important weight within the matching important weight range to a single service interaction matching process for information matching, and allocating the object tasks with the matching important weight not within the matching important weight range to a global service interaction matching process for information matching, so as to obtain the matched service access interaction characteristics.

Based on the above steps, in this embodiment, by determining the interactive service tag of the operation request interactive element, a queue comparison parameter formed between different service interaction matching processes is monitored, and when the queue comparison parameter reaches a certain condition, the operation request interactive element with different matching important weights is allocated to different service interaction matching processes according to the queue comparison parameter for parallel processing, so that the matching pressure of the service interaction matching processes is relieved, and the matching efficiency is greatly improved.

In one possible implementation, step S113 may be implemented by the following exemplary substeps, for example, as described in detail below.

And a substep S1131, obtaining a fault interaction feature vector of the online communication fault data set through a second service feature extraction unit included in the service relationship identification model.

In this embodiment, the feature vectors of the access target object that has initiated access on the online communication service terminal 200 on the plurality of fault determination rules are recorded in the fault interaction feature vector.

And a sub-step S1132, determining a target fault interaction feature vector corresponding to the feature vector on the target fault judgment rule according to the fault interaction feature vector of the online communication fault data set.

And in the substep S1133, summarizing all target fault interaction feature vectors to obtain an online communication fault interaction feature set under the condition that the similarity between the access target object recorded by the target fault interaction feature vectors and the known fault feature vectors of the target type reaches the set similarity.

In one possible implementation, step S114 may be implemented, for example, by the following exemplary substeps, which are described in detail below.

And a substep S11401, aiming at each service access interaction feature in the service access interaction feature set, obtaining first process node matching information through a process node matching layer included in a first service authentication process unit, wherein the first service authentication process unit belongs to a service relationship identification model.

And a substep S11402 of obtaining, for each service access interaction feature in the service access interaction feature set, first process permission feature information through a process permission matching layer included in the first service authentication process unit.

And a substep S11403 of acquiring, for each service access interaction feature in the service access interaction feature set, first authentication relationship information through an authentication relationship layer included in the first service authentication process unit based on the first process node matching information and the first process authority feature information.

And a substep S11404, for each service access interaction feature in the service access interaction feature set, obtaining first communication authentication information through a first process permission matching layer included in the first service authentication process unit based on the first authentication relationship information and the service access interaction feature, wherein each first communication authentication information corresponds to one service access interaction feature.

And a substep S11405 of acquiring, based on the online communication fault interaction feature set, second process node matching information through a process node matching layer included in a second service authentication process unit for each online communication fault interaction feature in the online communication fault interaction feature set, wherein the second service authentication process unit belongs to the service relationship identification model.

And a substep S11406, obtaining second process permission feature information through a process permission matching layer included in the second service authentication process unit for each online communication fault interaction feature in the online communication fault interaction feature set.

And a substep S11407, for each online communication fault interaction feature in the online communication fault interaction feature set, obtaining second authentication relationship information through an authentication relationship layer included in the second service authentication process unit based on the second process node matching information and the second process authority feature information.

And a substep S11408, for each online communication fault interaction feature in the online communication fault interaction feature set, obtaining second communication authentication information through a second process permission matching layer included in the second service authentication process unit based on the second authentication relationship information and the online communication fault interaction feature, wherein each piece of second communication authentication information corresponds to one online communication fault interaction feature.

And a substep S11409 of performing matching processing on a preset number of pieces of first communication authentication information and a preset number of pieces of second communication authentication information to obtain a preset number of pieces of matched target communication authentication information, wherein each piece of target communication authentication information includes one piece of first communication authentication information and one piece of second communication authentication information.

And a substep S11410 of obtaining a preset number of first sub-communication authentication information through a first authentication positioning layer included in an authentication positioning unit based on a preset number of target communication authentication information, wherein the authentication positioning unit belongs to a service relationship identification model.

And a substep S11411 of obtaining a predetermined number of second sub-communication authentication information through a second authentication positioning layer included in the authentication positioning unit based on the predetermined number of first sub-communication authentication information.

And a substep S11412 of determining a preset number of authentication nodes according to a preset number of second sub-communication authentication information, wherein each authentication node corresponds to one target communication authentication information.

And a substep S11413 of determining authentication relationship communication triggering information according to a preset number of target communication authentication information and a preset number of authentication nodes.

And a substep S11414, based on the authentication relationship communication trigger information, obtaining an optimized communication interaction object corresponding to the service access big data set through an information classification unit included in the service relationship identification model.

In another possible implementation manner, step S114 may be implemented by the following exemplary sub-steps, for example, which are described in detail below.

And a substep S11415, acquiring a preset number of first communication authentication information through a first process permission matching layer included in the service relationship identification model based on the service access interaction feature set, wherein each first communication authentication information corresponds to one service access interaction feature.

And a substep S11416, acquiring a preset number of second communication authentication information through a second process permission matching layer included in the service relationship identification model based on the online communication fault interaction feature set, wherein each second communication authentication information corresponds to one online communication fault interaction feature.

And a substep S11417 of performing matching processing on a preset number of pieces of first communication authentication information and a preset number of pieces of second communication authentication information to obtain a preset number of pieces of matched target communication authentication information, wherein each piece of target communication authentication information includes one piece of first communication authentication information and one piece of second communication authentication information.

And a substep S11418 of obtaining, based on a preset number of target communication authentication information, authentication relationship communication trigger information through an authentication positioning unit included in the service relationship identification model, wherein the authentication relationship communication trigger information is determined according to the preset number of target communication authentication information and the preset number of authentication nodes, and each target communication authentication information corresponds to one authentication node.

And a substep S11419 of obtaining an optimized communication interaction object corresponding to the service access big data set through an information classification unit included in the service relation identification model based on the authentication relation communication trigger information.

In a possible implementation manner, for step S120, each preset communication optimization script includes a communication optimization entity of a different communication optimization signature item and communication optimization entity information preconfigured by the communication optimization entity. Based on this, in the process of obtaining communication optimization entity information between the communication optimization signature item corresponding to the multiple optimized communication interaction objects and the multiple preset communication optimization scripts, a communication optimization entity of the communication optimization signature item corresponding to each optimized communication interaction object can be obtained from the multiple preset communication optimization scripts respectively, and then communication optimization entity information pre-configured in the preset communication optimization scripts by each communication optimization entity is obtained from the multiple preset communication optimization scripts respectively.

In a possible implementation manner, for step S130, an optimization importance degree between each communication optimization signature item and each preset communication optimization script may be determined based on the communication optimization entity information, a communication optimization feature vector of each communication optimization signature item and a communication optimization feature vector of each preset communication optimization script may be obtained based on the optimization importance degree, a similar communication optimization signature item of each communication optimization signature item may be determined according to the first preset artificial intelligence model based on the communication optimization feature vector of each communication optimization signature item, a similar communication optimization policy corresponding to each communication optimization signature item may be determined according to the second preset artificial intelligence model based on the communication optimization feature vector of each preset communication optimization script.

It is worth to be noted that the target communication optimization strategy includes a preset communication optimization script of the communication optimization entity corresponding to each communication optimization signature item, a preset communication optimization script of the communication optimization entity corresponding to a similar communication optimization signature item, and a similar communication optimization strategy of each communication optimization signature item.

The preset artificial intelligence model comprises a first preset artificial intelligence model and a second preset artificial intelligence model, the first preset artificial intelligence model is obtained based on the training of the communication optimization signature item labels corresponding to the first training samples and each first training sample, and the second preset artificial intelligence model is obtained based on the training of the communication optimization strategy labels corresponding to the second training samples and each second training sample.

Thus, based on the above sub-steps, the present embodiment obtains the communication optimization feature vector of each communication optimization signature item and the communication optimization feature vector of each preset communication optimization script based on the optimization importance degree in the foregoing manner, and obtains the communication optimization feature vector of each communication optimization signature item based on the communication optimization feature vector of each communication optimization signature item, determining similar communication optimization signature items of each communication optimization signature item according to a first preset artificial intelligence model, based on the communication optimization feature vector of each preset communication optimization script, determining a similar communication optimization strategy corresponding to each communication optimization signature item according to a second preset artificial intelligence model, in order to make the generated communication optimization feature vector have better real-time synchronicity of the communication optimization strategy, and then the effect of more accurately pushing the target communication optimization strategy can be realized through the communication optimization characteristic vector.

In a possible implementation manner, further in step S130, before determining the optimization importance degree between each communication optimization signature item and each preset communication optimization script based on the communication optimization entity information, in order to perform further optimization, improve reliability, and reduce computation amount, the embodiment may further delete the communication optimization entity to be processed in the communication optimization entity based on the entity optimization configuration information, so as to obtain processed communication optimization entity information.

Illustratively, the communication optimization entity to be processed specifies the communication optimization entity whose entity optimization configuration information in the communication optimization entity of the communication optimization strategy does not satisfy the specified service condition, and the specified communication optimization strategy is a preset communication optimization script whose corresponding communication optimization signature item quantity is greater than the communication optimization signature item threshold value in a plurality of preset communication optimization scripts. It should be noted that the designated service condition includes that the entity optimization configuration information is in a first designated service interval starting with the designated service node, and an ending service node of the first designated service interval is a current service node.

Thus, in S130, in the process of determining the optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively based on the communication optimization entity information, the optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively may be determined based on the processed communication optimization entity information.

In a possible implementation manner, further in step S120, in order to perform further optimization, improve reliability, and reduce computation amount, before obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, the embodiment may further obtain a communication optimization script to be processed in the plurality of communication optimization scripts, where the communication optimization script to be processed does not have a corresponding communication optimization entity matching in a second specified service interval. And then deleting the communication optimization script to be processed in the communication optimization scripts to obtain a plurality of preset communication optimization scripts.

In a possible implementation manner, further in step S130, in the process of determining the optimization importance degree between each communication optimization signature item and each preset communication optimization script respectively based on the communication optimization entity information, the embodiment may obtain the occupation proportion of each communication optimization entity between the communication optimization signature item currently undergoing the optimization importance degree calculation and the preset communication optimization script currently undergoing the optimization importance degree calculation. Then, the occupation proportion of each communication optimization entity can be added to obtain the optimization importance degree between the communication optimization signature item currently subjected to optimization importance degree calculation and the preset communication optimization script currently subjected to optimization importance degree calculation.

In a possible implementation manner, further in step S130, in the process of obtaining the communication optimization feature vector of each communication optimization signature item and the communication optimization feature vector of each preset communication optimization script based on the optimization importance degree, the following exemplary embodiments may be implemented:

(1) and acquiring an initial communication optimization characteristic vector of each communication optimization signature item and an initial communication optimization characteristic vector of each preset communication optimization script.

(2) And acquiring the matching confidence of any communication optimization signature item and any communication optimization strategy on the basis of the initial communication optimization feature vector of any communication optimization signature item in the communication optimization signature items and any communication optimization script in the communication optimization scripts in sequence to obtain global matching confidence distribution.

(3) And obtaining the optimized node distribution of any communication optimized signature item and any communication optimized strategy based on the optimized importance degree of any communication optimized signature item in the communication optimized signature items and any communication optimized strategy in the preset communication optimized scripts and the first optimized node distribution evaluation rule in sequence to obtain the first global optimized node distribution.

(4) And obtaining the difference between the global matching confidence coefficient distribution and the first global optimization node distribution based on the first distribution difference evaluation function, and updating the initial communication optimization feature vector based on the difference to obtain a first communication optimization feature vector to be fused.

(5) And acquiring the matching degree of any communication optimization signature item and any communication optimization strategy on the basis of the initial communication optimization feature vector of any communication optimization signature item in the communication optimization signature items and any communication optimization strategy in the preset communication optimization scripts in sequence to obtain the global matching degree distribution.

(6) And obtaining the optimized node distribution of any communication optimized signature item and any communication optimized strategy based on the optimized importance degree of any communication optimized signature item in the communication optimized signature items and any communication optimized strategy in the preset communication optimized scripts and the second optimized node distribution evaluation rule in sequence to obtain the second global optimized node distribution.

(7) And obtaining the difference between the global matching degree distribution and the second global optimization node distribution based on the second distribution difference evaluation function, and updating the initial communication optimization feature vector based on the difference to obtain a second communication optimization feature vector to be fused.

(8) And fusing the first communication optimization feature vector to be fused with the second communication optimization feature vector to be fused to obtain each communication optimization signature item and each communication optimization feature vector corresponding to each preset communication optimization script.

Based on the same inventive concept, please refer to fig. 3, which illustrates a functional module diagram of an information processing apparatus 300 based on remote communication and artificial intelligence provided in the embodiment of the present application, and the embodiment can perform functional module division on the information processing apparatus 300 based on remote communication and artificial intelligence according to the above method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module by corresponding each function, the telematics-based information processing apparatus 300 shown in fig. 3 is only an apparatus diagram. The telematics-based information processing apparatus 300 may include a first obtaining module 310, a second obtaining module 320, a generating module 330, and an information generating module 340, wherein functions of the functional modules of the telematics-based information processing apparatus 300 are described in detail below.

The first obtaining module 310 is configured to obtain an optimized communication interaction object corresponding to service access big data uploaded by an online communication service terminal. It is understood that the first obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the first obtaining module 310, reference may be made to the content related to the step S110.

The second obtaining module 320 is configured to obtain communication optimization entity information between communication optimization signature items corresponding to the multiple optimized communication interaction objects and the multiple preset communication optimization scripts, where the communication optimization entity information includes communication optimization entities corresponding to the multiple communication optimization signature items and the multiple preset communication optimization scripts, and entity optimization configuration information of the communication optimization entities. It is understood that the second obtaining module 320 may be configured to perform the step S120, and for a detailed implementation of the second obtaining module 320, reference may be made to the content related to the step S120.

The generating module 330 is configured to process, according to a preset artificial intelligence model, communication optimization entity information between communication optimization signature items corresponding to multiple optimized communication interaction objects and multiple preset communication optimization scripts, generate a corresponding target communication optimization policy, and send the target communication optimization policy to the online communication service terminal, so that the online communication service terminal performs communication optimization of parameter update contents corresponding to each communication optimization element specified in the target communication optimization policy on each communication optimization element based on the target communication optimization policy. It is understood that the generating module 330 may be configured to perform the step S130, and for the detailed implementation of the generating module 330, reference may be made to the content related to the step S130.

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

For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).

Fig. 4 is a schematic diagram illustrating a hardware structure of the cloud service platform 100 for implementing the information processing method based on remote communication and artificial intelligence according to the embodiment of the present invention, and as shown in fig. 4, the cloud service platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, the at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the first obtaining module 310, the second obtaining module 320, the generating module 330, and the information generating module 340 included in the telematics-based information processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the telematics-based and artificial intelligence information processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to transceive data with the online communication terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the cloud service platform 100, and implementation principles and technical effects are similar, which are not described herein again.

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

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

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

In addition, the embodiment of the invention also provides a readable storage medium, wherein the readable storage medium stores computer execution instructions, and when a processor executes the computer execution instructions, the information processing method based on remote communication and artificial intelligence is realized.

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

Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

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

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

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may run entirely on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or cloud service platform. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

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

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

It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

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

Claims

1. An information processing method based on remote communication and artificial intelligence is applied to a cloud service platform in communication connection with a plurality of online communication service terminals, and comprises the following steps:

2. The information processing method based on telecommunication and artificial intelligence as claimed in claim 1, wherein the step of obtaining the optimized communication interaction object corresponding to the service access big data uploaded by the online communication service terminal comprises:

3. The information processing method based on telecommunication and artificial intelligence as claimed in claim 2, wherein the step of obtaining the optimized communication interaction object corresponding to the service access big data through the information classification unit included in the business relationship identification model based on the service access interaction feature set and the online communication failure interaction feature set comprises:

4. The information processing method based on telecommunication and artificial intelligence as claimed in any one of claims 1-3, wherein the step of processing the communication optimization entity information between the communication optimization signature items corresponding to the plurality of optimized communication interaction objects and the plurality of preset communication optimization scripts according to a preset artificial intelligence model to generate the corresponding target communication optimization strategy comprises:

5. The method of claim 4, wherein before the step of determining the importance of each communication optimization signature item to each communication optimization script based on the communication optimization entity information, the method further comprises:

6. The method for processing information based on telecommunication and artificial intelligence as claimed in claim 1, wherein before the step of obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts, the method further comprises:

7. The method for processing information based on telecommunication and artificial intelligence as claimed in claim 4, wherein said determining the optimized importance degree between each communication optimization signature item and each preset communication optimization script respectively based on the communication optimization entity information comprises:

8. The method for processing information based on telecommunication and artificial intelligence as claimed in claim 4, wherein said obtaining the communication optimization feature vector of each communication optimization signature item and the communication optimization feature vector of each preset communication optimization script based on the optimization importance degree comprises:

9. The method for processing information based on telecommunication and artificial intelligence as claimed in any one of claims 1-8, wherein the step of obtaining communication optimization entity information between communication optimization signature items corresponding to a plurality of optimized communication interaction objects and a plurality of preset communication optimization scripts comprises:

10. A cloud service platform, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one online communication service terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the information processing method based on telecommunication and artificial intelligence according to any one of claims 1 to 9.