CN113051386A - Adjustment method and device for manual service conversion, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a manual service conversion adjusting method and device, electronic equipment and a storage medium. In the manual service switching adjustment method, when the interaction between the user and the robot is detected, the interaction process between the user and the robot is monitored, and the user behavior information of the user and the interaction information input by the user are acquired. The interactive information comprises text information and voice information. And analyzing the interactive information input by the user to obtain the emotion value of the user. And detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result. And adjusting parameters of the automatic switching manual service conditions of the user based on the detection result. And taking the adjusted automatic switching manual service condition as an automatic switching manual service condition of the user.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to an adjustment method and apparatus for transferring an artificial service, an electronic device, and a storage medium.
Background
Human-computer interaction is the science of studying the interactive relationships between systems and users. The system may be a variety of machines, and may be a computerized system and software. For example, various artificial intelligence systems can be implemented through human-computer interaction, wherein an intelligent question-answering system is a typical application of human-computer interaction, and when a user proposes a question, the intelligent question-answering system finds a question corresponding to the question of the user from a knowledge base and then finds an answer matched with the question.
With the wide application of the intelligent question-answering system, more and more enterprises set up intelligent robot customer service through the intelligent question-answering system to ask questions and ask puzzles for users. However, the robot customer service can only search according to the information input by the user due to the data limitation of the knowledge base, and when the data in the knowledge base cannot search the answer required by the user, the robot customer service can only solve the answer by switching to the manual customer service. In the process of customer service interaction with a robot, the setting of manual service conditions is often fixed, but because the tolerance of each user is different, some users can generate discontent emotion to the current interaction when the manual service conditions are not triggered, and the satisfaction degree of the users to enterprises is reduced.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for adjusting manual service, an electronic device, and a storage medium, so as to solve the problem in the prior art that a user has generated an unsatisfied emotion to a current interaction when a manual-switching condition is not triggered, and reduce the satisfaction of the user to an enterprise.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application discloses in a first aspect a method for adjusting manual service, comprising:
when detecting that a user interacts with the robot, monitoring the interaction process of the user and the robot, and acquiring user behavior information of the user and interaction information input by the user; the interactive information comprises text information and voice information;
analyzing the interactive information input by the user to obtain the emotion value of the user;
detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result;
adjusting parameters of the automatic switching manual service conditions of the user based on the detection result; wherein the lower the parameter, the easier the automatic switchover manual service condition is triggered, and the higher the parameter, the less easy the automatic switchover manual service condition is triggered;
and taking the adjusted automatic switching manual service condition as the automatic switching manual service condition of the user.
Optionally, in the method, the analyzing the interactive information input by the user to obtain the emotion value of the user includes:
when the text information is input by the user, performing word segmentation on the text information;
performing text vectorization on the text after word segmentation by using a language model in the field of customer service to obtain a text vector;
inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user; the text emotion model is constructed in advance according to a text data set labeled with text emotion.
Optionally, in the method, the analyzing the interactive information input by the user to obtain the emotion value of the user includes:
when the voice information is input by the user, extracting the voiceprint feature data of the user;
inputting the voiceprint feature data into a voice emotion model which is constructed in advance to obtain an emotion value of the user; the voice emotion model is obtained by constructing in advance according to a voiceprint feature data set marked with voice emotion.
Optionally, in the method, a process of constructing the text emotion model includes:
acquiring a text data set of the marked text emotion;
inputting the text data set labeled with the text emotion into an initial model for operation to obtain an emotion value of the current text data;
judging whether the emotion value of the current text data is consistent with the emotion value of the actually marked text data;
if the emotion value of the current text data is consistent with the emotion value of the actually marked text data, the construction of the text emotion model is completed;
and if the emotion value of the current text data is not consistent with the emotion value of the actually labeled text data, solving an error function, and adjusting the parameters of the neural network model by using the error function until the output emotion value of the current text data is consistent with the emotion value of the actually labeled text data, so that the construction of the text emotion model is completed.
Optionally, in the foregoing method, the adjusting the parameter of the automatic manual service switching condition of the user based on the detection result includes:
if the emotion value of the user is detected to be lower than a preset threshold value and the user behavior information of the user does not trigger the automatic switching manual service condition, reducing the parameter of the automatic switching manual service condition of the user;
and if the situation value of the user is not lower than a preset threshold value and the user behavior information of the user triggers the automatic switching manual service condition, increasing the parameters of the automatic switching manual service condition of the user.
The second aspect of the present application discloses an adjusting device for transferring manual service, comprising:
the monitoring unit is used for monitoring the interaction process of the user and the robot when detecting that the user interacts with the robot, and acquiring the user behavior information of the user and the interaction information input by the user; the interactive information comprises text information and voice information;
the analysis unit is used for analyzing the interactive information input by the user to obtain the emotion value of the user;
the detection unit is used for detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result;
the adjusting unit is used for adjusting the parameters of the automatic switching manual service conditions of the user based on the detection result; wherein the lower the parameter, the easier the automatic switchover manual service condition is triggered, and the higher the parameter, the less easy the automatic switchover manual service condition is triggered;
and the determining unit is used for taking the adjusted automatic switching manual service condition as the automatic switching manual service condition of the user.
Optionally, in the foregoing apparatus, the parsing unit includes:
the first information processing subunit is used for segmenting the text information when the text information is input by the user;
the second information processing subunit is used for performing text vectorization on the text subjected to word segmentation by using a language model in the customer service field to obtain a text vector;
the first operation subunit is used for inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user; the text emotion model is constructed in advance according to a text data set labeled with text emotion.
Optionally, in the foregoing apparatus, the parsing unit includes:
the data extraction unit is used for extracting the voiceprint characteristic data of the user when the voice information is input by the user;
the second operation subunit is used for inputting the voiceprint characteristic data into a voice emotion model which is constructed in advance to obtain an emotion value of the user; the voice emotion model is obtained by constructing in advance according to a voiceprint feature data set marked with voice emotion.
Optionally, in the apparatus, the first operation subunit includes:
the obtaining subunit is used for obtaining the text data set of the marked text emotion;
the third operation subunit is used for inputting the text data set labeled with the text emotion into the initial model for operation to obtain the emotion value of the current text data;
the judging subunit is used for judging whether the emotion value of the current text data is consistent with the emotion value of the actually labeled text data;
the construction subunit is used for finishing the construction of the text emotion model if the emotion value of the current text data is consistent with the emotion value of the actually labeled text data;
and the parameter adjusting subunit is used for solving an error function if the emotion value of the current text data is inconsistent with the emotion value of the actually labeled text data, and adjusting the parameters of the neural network model by using the error function until the output emotion value of the current text data is consistent with the emotion value of the actually labeled text data, so as to complete the construction of the text emotion model.
Optionally, in the foregoing apparatus, the adjusting unit includes:
the first adjusting subunit is used for reducing the parameters of the automatic transfer manual service conditions of the user if the emotion value of the user is detected to be lower than a preset threshold value and the user behavior information of the user does not trigger the automatic transfer manual service conditions;
and the second adjusting subunit is used for increasing the parameters of the automatic transfer manual service conditions of the user if the situation value of the user is detected to be not lower than the preset threshold value and the user behavior information of the user triggers the automatic transfer manual service conditions.
A third aspect of the present application discloses an electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of the first aspects of the present invention.
A fourth aspect of the present application discloses a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of the first aspect of the present invention.
According to the technical scheme, in the manual service switching adjustment method provided by the application, when the interaction between the user and the robot is detected, the interaction process between the user and the robot is monitored, and the user behavior information of the user and the interaction information input by the user are acquired. The interactive information comprises text information and voice information. And analyzing the interactive information input by the user to obtain the emotion value of the user. And detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result. And adjusting parameters of the automatic switching manual service conditions of the user based on the detection result. Wherein, the lower the parameter, the easier the automatic transfer manual service condition is triggered, and the higher the parameter, the less easy the automatic transfer manual service condition is triggered. And taking the adjusted automatic switching manual service condition as an automatic switching manual service condition of the user. Therefore, by using the method, when the user interacts with the robot customer service, whether the emotion value of the user is lower than a preset threshold value in the interaction process of the user and the robot customer service and whether the user behavior information of the user triggers the automatic switching manual service condition can be detected in real time, the automatic switching manual service condition parameter of the user is adjusted according to the detection result, the purpose of setting the exclusive automatic switching manual service condition for the user according to the user condition is achieved, and the user experience is improved. The method and the device solve the problems that in the prior art, the user generates discontented emotion to the current interaction when the manual condition is not triggered, and the satisfaction degree of the user to an enterprise is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of an adjustment method for transferring manual service according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of one embodiment of a process for constructing a textual emotion model, as disclosed in another embodiment of the present application;
FIG. 3 is a schematic diagram of an adjusting apparatus for manual service transfer according to another embodiment of the present disclosure;
fig. 4 is a schematic diagram of an electronic device according to another embodiment of the disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Moreover, in this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
As can be seen from the background art, with the wide application of the intelligent question-answering system, more and more enterprises set up intelligent robot customer service through the intelligent question-answering system to ask questions and solve puzzles for users. However, the robot customer service can only search according to the information input by the user due to the data limitation of the knowledge base, and when the data in the knowledge base cannot search the answer required by the user, the robot customer service can only solve the answer by switching to the manual customer service. In the process of customer service interaction with a robot, the setting of manual service conditions is often fixed, but because the tolerance of each user is different, some users can generate discontent emotion to the current interaction when the manual service conditions are not triggered, and the satisfaction degree of the users to enterprises is reduced.
In view of this, the present application provides a method and an apparatus for adjusting manual-to-manual service, an electronic device, and a storage medium, so as to solve the problem in the prior art that a user has generated an unsatisfied emotion to a current interaction when a manual-to-manual condition is not triggered, and reduce the satisfaction of the user to an enterprise.
The embodiment of the present application provides an adjustment method for converting manual service, as shown in fig. 1, specifically including:
s101, when detecting that a user interacts with the robot, monitoring the interaction process of the user and the robot, and acquiring user behavior information of the user and interaction information input by the user; the interactive information comprises text information and voice information.
It should be noted that, when it is detected that the incoming line of the user interacts with the customer service of the robot, the interaction process between the user and the robot is monitored in real time, and meanwhile, the user behavior information of the user and the interaction information input by the user are acquired. The interactive information comprises text information and voice information, such as online character interaction or telephone voice interaction between a user and the robot customer service. The behavior information of the user includes what operations the user has done during the interaction, such as scoring the robot customer service, actively performing manual service operation, and the like.
And S102, analyzing the interactive information input by the user to obtain the emotion value of the user.
It should be noted that after the interactive information input by the user each time is acquired, the interactive information input by the user is analyzed, so that the emotion value of the user reflected in the interactive information input by the user is analyzed and acquired. For example, the information input by the user includes words expressing emotion, such as mood words, which can reflect the current approximate emotional change of the user.
Optionally, in another embodiment of the present application, an implementation manner of step S102 includes:
when the user inputs text information, performing word segmentation on the text information; performing text vectorization on the text after word segmentation by using a language model in the field of customer service to obtain a text vector; and inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user.
It should be noted that, when text information is input by the user, word segmentation is performed on the acquired text information, and then text vectorization is performed on the text after word segmentation by using a language model in the customer service field to obtain a text vector. Inputting the text vector into a pre-constructed text emotion model for operation, so as to obtain an emotion value of the user; the text emotion model is obtained by constructing a text data set labeled with text emotion in advance, and the text emotion model adopts a neural network structure of biGRU + Attention layer + full connection layer.
Optionally, in another embodiment of the present application, an implementation manner of step S102 includes:
when the user inputs voice information, voice print characteristic data of the user is extracted.
Inputting the voiceprint feature data into a voice emotion model which is constructed in advance to obtain an emotion value of a user; the voice emotion model is constructed in advance according to a voiceprint feature data set marked with voice emotion.
It should be noted that, when the user inputs voice information, the voice information is first converted into text information by a voice recognition technology, then the text information is subjected to word segmentation, and after the word segmentation is completed, the voiceprint feature data of the user is extracted in a segmentation manner by the result of text word segmentation under the dark war. And then inputting the extracted voiceprint feature data into a voice emotion model which is constructed in advance for operation, so that the emotion value of the user can be obtained. The voice emotion model is obtained by constructing in advance according to a voiceprint feature data set marked with voice emotion, and the voice emotion model adopts a neural network structure of biGRU + Attention layer + full connection layer.
Optionally, in another embodiment of the present application, a construction manner of the text emotion model in the above step, as shown in fig. 2, includes:
s201, acquiring a text data set of the marked text emotion.
First, a text data set of text emotion is labeled, and these text data are input to a neural network model as training data to be trained.
S202, inputting the text data set labeled with the text emotion into the initial model for operation to obtain the emotion value of the current text data.
S203, judging whether the emotion value of the current text data is consistent with the emotion value of the actually labeled text data.
It should be noted that, after the emotion value of the current text data is obtained through operation in the initial operation model, the emotion value of the text data obtained through operation is compared with the emotion value of the text data actually labeled, so as to determine whether the emotion value of the text data obtained through operation in the initial operation model is accurate.
And S204, if the emotion value of the current text data is consistent with the emotion value of the actually labeled text data, the construction of a text emotion model is completed.
S205, if the emotion value of the current text data is not consistent with the emotion value of the actually labeled text data, an error function is solved, the parameters of the neural network model are adjusted by using the error function until the emotion value of the output current text data is consistent with the emotion value of the actually labeled text data, and the construction of the text emotion model is completed.
If the emotion value of the calculated text data is not consistent with the emotion value of the actually labeled text data, an error function of the neural network model is solved, parameters of the neural network model are adjusted by using the error function, a new round of operation is performed again until the emotion value of the output text data is consistent with the emotion value of the actually labeled text data, and the construction of the text emotion model is completed.
S103, detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot, and obtaining a detection result.
It should be noted that, in the process of interaction between the user and the robot, it is detected in real time whether the emotion value of the user is lower than a preset threshold, the threshold may be set according to an actual situation, and the emotion of the user is deteriorated as described by being lower than the threshold. And detecting whether the user behavior information of the user triggers the automatic switching manual service condition or not to obtain a detection result. The automatic switching manual service condition can set the number of times that the scoring value of the robot is lower than the threshold value a by the user to reach n times; the user repeatedly asks a question m times; the robot repeatedly matches a question p times, etc., where the values of a, n, m, p can be set according to the actual situation.
S104, adjusting parameters of automatic switching manual service conditions of the user based on the detection result; wherein, the lower the parameter, the easier the automatic transfer manual service condition is triggered, and the higher the parameter, the less easy the automatic transfer manual service condition is triggered.
It should be noted that, after the detection result is obtained, some appropriate adjustments are made to the parameters of the automatic switching manual service condition of the current user according to the behavior and emotion change of the user in the interaction process. For example, if the emotion of the user becomes worse, which indicates that the user may be dissatisfied with the robot service, the parameters of the automatic switching manual service condition of the user should be adjusted, so that the automatic switching manual service condition is easier to trigger for the user.
Optionally, in another embodiment of the present application, an implementation manner of step S104 specifically includes:
and if the emotion value of the user is detected to be lower than the preset threshold value and the user behavior information of the user does not trigger the automatic switching manual service condition, reducing the parameters of the automatic switching manual service condition of the user.
And if the emotion value of the user is not lower than the preset threshold value and the user behavior information of the user triggers the automatic switching manual service condition, increasing the parameters of the automatic switching manual service condition of the user.
It should be noted that, if it is detected that the emotion value of the user is lower than the preset threshold value and the user behavior information of the user does not trigger the automatic manual service switching condition, which indicates that the user is not satisfied in the interaction of the robot, the parameter of the automatic manual service switching condition of the user should be reduced, so that the user can switch the manual service more easily. If the emotion value of the user is not lower than the preset threshold value and the user behavior information of the user triggers the automatic switching manual service condition, the user is satisfied in the interaction of the robot, and the parameters of the automatic switching manual service condition of the user can be properly increased.
And S105, taking the adjusted automatic switching manual service condition as an automatic switching manual service condition of the user.
It should be noted that, after the automatic transfer manual service condition is adjusted, the adjusted automatic transfer manual service condition is used as an automatic transfer manual service condition dedicated to the current user, and if the user enters a line again later, the adjusted automatic transfer manual service condition is the most automatic transfer manual service condition of the user.
In the adjustment method for transferring to manual service provided by the embodiment of the application, when the interaction between the user and the robot is detected, the interaction process between the user and the robot is monitored, and the user behavior information of the user and the interaction information input by the user are acquired. The interactive information comprises text information and voice information. And analyzing the interactive information input by the user to obtain the emotion value of the user. And detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result. And adjusting parameters of the automatic switching manual service conditions of the user based on the detection result. Wherein, the lower the parameter, the easier the automatic transfer manual service condition is triggered, and the higher the parameter, the less easy the automatic transfer manual service condition is triggered. And taking the adjusted automatic switching manual service condition as an automatic switching manual service condition of the user. Therefore, by using the method, when the user interacts with the robot customer service, whether the emotion value of the user is lower than a preset threshold value in the interaction process of the user and the robot customer service and whether the user behavior information of the user triggers the automatic switching manual service condition can be detected in real time, the automatic switching manual service condition parameter of the user is adjusted according to the detection result, the purpose of setting the exclusive automatic switching manual service condition for the user according to the user condition is achieved, and the user experience is improved. The method and the device solve the problems that in the prior art, the user generates discontented emotion to the current interaction when the manual condition is not triggered, and the satisfaction degree of the user to an enterprise is reduced.
Another embodiment of the present application further discloses an adjusting device for converting manual service, as shown in fig. 3, specifically including:
the monitoring unit 301 is configured to monitor an interaction process between a user and the robot when it is detected that the user interacts with the robot, and acquire user behavior information of the user and interaction information input by the user; the interactive information comprises text information and voice information.
The analyzing unit 302 is configured to analyze the interactive information input by the user to obtain an emotion value of the user.
The detecting unit 303 is configured to detect whether an emotion value of the user is lower than a preset threshold value and whether user behavior information of the user triggers an automatic manual service switching condition in an interaction process between the user and the robot, so as to obtain a detection result.
An adjusting unit 304, configured to adjust a parameter of an automatic switching manual service condition of the user based on the detection result; wherein, the lower the parameter, the easier the automatic transfer manual service condition is triggered, and the higher the parameter, the less easy the automatic transfer manual service condition is triggered.
A determining unit 305, configured to use the adjusted automatic transfer manual service condition as an automatic transfer manual service condition of the user.
In this embodiment, the specific implementation processes of the monitoring unit 301, the analyzing unit 302, the detecting unit 303, the adjusting unit 304, and the determining unit 305 may refer to the contents of the method embodiment corresponding to fig. 1, and are not described herein again.
In the adjustment device for converting manual service provided in the embodiment of the present application, when it is detected that a user interacts with a robot, the monitoring unit 301 monitors an interaction process between the user and the robot, and obtains user behavior information of the user and interaction information input by the user. The interactive information comprises text information and voice information. The analyzing unit 302 analyzes the interactive information input by the user to obtain an emotion value of the user. The detection unit 303 detects whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic transfer manual service condition in the interaction process between the user and the robot, so as to obtain a detection result. Based on the detection result, the adjusting unit 304 adjusts the parameters of the automatic switchover manual service condition of the user. Wherein, the lower the parameter, the easier the automatic transfer manual service condition is triggered, and the higher the parameter, the less easy the automatic transfer manual service condition is triggered. The determination unit 305 takes the adjusted automatic transfer manual service condition as an automatic transfer manual service condition of the user. Therefore, by using the method, when the user interacts with the robot customer service, whether the emotion value of the user is lower than a preset threshold value in the interaction process of the user and the robot customer service and whether the user behavior information of the user triggers the automatic switching manual service condition can be detected in real time, the automatic switching manual service condition parameter of the user is adjusted according to the detection result, the purpose of setting the exclusive automatic switching manual service condition for the user according to the user condition is achieved, and the user experience is improved. The method and the device solve the problems that in the prior art, the user generates discontented emotion to the current interaction when the manual condition is not triggered, and the satisfaction degree of the user to an enterprise is reduced.
Optionally, in another embodiment of the present application, an implementation manner of the parsing unit 302 includes:
and the first information processing subunit is used for segmenting the text information when the text information is input by the user.
And the second information processing subunit is used for performing text vectorization on the text subjected to word segmentation by using the language model in the customer service field to obtain a text vector.
The first operation subunit is used for inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user; the text emotion model is constructed in advance according to a text data set labeled with text emotion.
In this embodiment, for the specific execution process of the first information processing subunit, the second information processing subunit, and the first operation subunit, reference may be made to the contents of the above method embodiments, and details are not described here again.
Optionally, in another embodiment of the present application, an implementation manner of the parsing unit 302 includes:
and the data extraction unit is used for extracting the voiceprint characteristic data of the user when the voice information is input by the user.
The second operation subunit is used for inputting the voiceprint characteristic data into a voice emotion model which is constructed in advance to obtain an emotion value of the user; the voice emotion model is constructed in advance according to a voiceprint feature data set marked with voice emotion.
In this embodiment, for the specific execution processes of the data extraction unit and the second operation subunit, reference may be made to the contents of the above method embodiments, which are not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the first operation subunit includes:
and the acquiring subunit is used for acquiring the text data set labeled with the text emotion.
And the third operation subunit is used for inputting the text data set labeled with the text emotion into the initial model for operation to obtain the emotion value of the current text data.
And the judging subunit is used for judging whether the emotion value of the current text data is consistent with the emotion value of the actually labeled text data.
And the construction subunit is used for finishing construction of the text emotion model if the emotion value of the current text data is consistent with the emotion value of the actually labeled text data.
And the parameter adjusting subunit is used for solving an error function if the emotion value of the current text data is inconsistent with the emotion value of the actually labeled text data, and adjusting the parameters of the neural network model by using the error function until the emotion value of the output current text data is consistent with the emotion value of the actually labeled text data, so that the construction of the text emotion model is completed.
In this embodiment, the specific execution processes of the obtaining subunit, the calculating subunit, the judging subunit, the constructing subunit and the parameter adjusting subunit may refer to the contents of the method embodiment corresponding to fig. 2, and are not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the adjusting unit 304 includes:
and the first adjusting subunit is used for reducing the parameters of the automatic transfer manual service conditions of the user if the emotion value of the user is detected to be lower than the preset threshold value and the user behavior information of the user does not trigger the automatic transfer manual service conditions.
And the second adjusting subunit is used for increasing the parameters of the automatic switching manual service conditions of the user if the emotion value of the user is detected to be not lower than the preset threshold value and the user behavior information of the user triggers the automatic switching manual service conditions.
In this embodiment, for the specific execution processes of the first adjusting subunit and the second adjusting subunit, reference may be made to the contents of the corresponding method embodiments described above, and details are not described here again.
Another embodiment of the present application further provides an electronic device, as shown in fig. 4, specifically including:
one or more processors 401.
A storage device 402 having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors 401, cause the one or more processors 401 to implement the method as in any one of the embodiments described above.
Another embodiment of the present application further provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method according to any one of the above embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for adjusting manual service is characterized by comprising the following steps:
when detecting that a user interacts with the robot, monitoring the interaction process of the user and the robot, and acquiring user behavior information of the user and interaction information input by the user; the interactive information comprises text information and voice information;
analyzing the interactive information input by the user to obtain the emotion value of the user;
detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result;
adjusting parameters of the automatic switching manual service conditions of the user based on the detection result; wherein the lower the parameter, the easier the automatic switchover manual service condition is triggered, and the higher the parameter, the less easy the automatic switchover manual service condition is triggered;
and taking the adjusted automatic switching manual service condition as the automatic switching manual service condition of the user.
2. The method of claim 1, wherein the analyzing the interaction information input by the user to obtain the emotion value of the user comprises:
when the text information is input by the user, performing word segmentation on the text information;
performing text vectorization on the text after word segmentation by using a language model in the field of customer service to obtain a text vector;
inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user; the text emotion model is constructed in advance according to a text data set labeled with text emotion.
3. The method of claim 1, wherein the analyzing the interaction information input by the user to obtain the emotion value of the user comprises:
when the voice information is input by the user, extracting the voiceprint feature data of the user;
inputting the voiceprint feature data into a voice emotion model which is constructed in advance to obtain an emotion value of the user; the voice emotion model is obtained by constructing in advance according to a voiceprint feature data set marked with voice emotion.
4. The method of claim 2, wherein the construction process of the text emotion model comprises:
acquiring a text data set of the marked text emotion;
inputting the text data set labeled with the text emotion into an initial model for operation to obtain an emotion value of the current text data;
judging whether the emotion value of the current text data is consistent with the emotion value of the actually marked text data;
if the emotion value of the current text data is consistent with the emotion value of the actually marked text data, the construction of the text emotion model is completed;
and if the emotion value of the current text data is not consistent with the emotion value of the actually labeled text data, solving an error function, and adjusting the parameters of the neural network model by using the error function until the output emotion value of the current text data is consistent with the emotion value of the actually labeled text data, so that the construction of the text emotion model is completed.
5. The method of claim 1, wherein the adjusting the parameters of the user's automatic diversion manual service conditions based on the detection result comprises:
if the emotion value of the user is detected to be lower than a preset threshold value and the user behavior information of the user does not trigger the automatic switching manual service condition, reducing the parameter of the automatic switching manual service condition of the user;
and if the situation value of the user is not lower than a preset threshold value and the user behavior information of the user triggers the automatic switching manual service condition, increasing the parameters of the automatic switching manual service condition of the user.
6. An adjustment device for manual service transfer, comprising:
the monitoring unit is used for monitoring the interaction process of the user and the robot when detecting that the user interacts with the robot, and acquiring the user behavior information of the user and the interaction information input by the user; the interactive information comprises text information and voice information;
the analysis unit is used for analyzing the interactive information input by the user to obtain the emotion value of the user;
the detection unit is used for detecting whether the emotion value of the user is lower than a preset threshold value and whether the user behavior information of the user triggers an automatic switching manual service condition in the interaction process of the user and the robot to obtain a detection result;
the adjusting unit is used for adjusting the parameters of the automatic switching manual service conditions of the user based on the detection result; wherein the lower the parameter, the easier the automatic switchover manual service condition is triggered, and the higher the parameter, the less easy the automatic switchover manual service condition is triggered;
and the determining unit is used for taking the adjusted automatic switching manual service condition as the automatic switching manual service condition of the user.
7. The apparatus of claim 6, wherein the parsing unit comprises:
the first information processing subunit is used for segmenting the text information when the text information is input by the user;
the second information processing subunit is used for performing text vectorization on the text subjected to word segmentation by using a language model in the customer service field to obtain a text vector;
the first operation subunit is used for inputting the text vector into a pre-constructed text emotion model to obtain an emotion value of the user; the text emotion model is constructed in advance according to a text data set labeled with text emotion.
8. The apparatus of claim 6, wherein the adjusting unit comprises:
the first adjusting subunit is used for reducing the parameters of the automatic transfer manual service conditions of the user if the emotion value of the user is detected to be lower than a preset threshold value and the user behavior information of the user does not trigger the automatic transfer manual service conditions;
and the second adjusting subunit is used for increasing the parameters of the automatic transfer manual service conditions of the user if the situation value of the user is detected to be not lower than the preset threshold value and the user behavior information of the user triggers the automatic transfer manual service conditions.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-5.
10. A computer storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 5.
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