CN113489851A - Information prompting method, device, electronic equipment, storage medium and program product - Google Patents
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- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
- H04M3/523—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing with call distribution or queueing
- H04M3/5238—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing with call distribution or queueing with waiting time or load prediction arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
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Abstract
The embodiment of the disclosure discloses an information prompting method, an information prompting device, electronic equipment, a storage medium and a program product, wherein the method comprises the following steps: determining one or more queuing objects in a current queue, and acquiring conversation contents between a preset conversation object and the queuing objects; estimating the call duration corresponding to the queuing object according to the call content; and calculating the waiting time of the queuing object according to the call time corresponding to the queuing object, and prompting the queuing object based on the waiting time of the queuing object. According to the technical scheme, the user can be accurately prompted about how long the user needs to wait, and the user experience and the platform service quality are improved.
Description
Technical Field
The disclosed embodiments relate to the technical field of information processing, and in particular, to an information prompting method, an information prompting device, an electronic device, a storage medium, and a program product.
Background
With the development of science and technology, users can seek services for various platforms through the channels of networks, telephones and the like, and service providers can often communicate with the users through the networks or the telephones, receive the consultation of the users, acquire the requirements of the users, solve the problems of the users and provide help for the users. In practical application, a user requesting manual customer service generally needs to wait in a queue, while the prior art generally only can prompt the user that a few people in front of the user wait, or prompt the user to queue the second place of the waiting queue, and cannot relatively accurately prompt the user how long the user needs to wait. This is not conducive to improving the user experience and improving the quality of service of the platform.
Disclosure of Invention
The embodiment of the disclosure provides an information prompting method, an information prompting device, electronic equipment, a storage medium and a program product.
In a first aspect, an embodiment of the present disclosure provides an information prompting method.
Specifically, the information prompting method includes:
determining one or more queuing objects in a current queue, and acquiring conversation contents between a preset conversation object and the queuing objects;
estimating the call duration corresponding to the queuing object according to the call content;
and calculating the waiting time of the queuing object according to the call time corresponding to the queuing object, and prompting the queuing object based on the waiting time of the queuing object.
With reference to the first aspect, in a first implementation manner of the first aspect, the predicting, according to the call content, a call duration corresponding to the queuing object includes:
acquiring the characteristic data of the call content, wherein the characteristic data of the call content comprises one or more of the following data: call category, call keyword;
and inputting the characteristic data of the call content into a pre-trained call duration estimation model to obtain the call duration corresponding to the queuing object.
With reference to the first aspect and the first implementation manner of the first aspect, in a second implementation manner of the first aspect, an embodiment of the present disclosure further includes:
and training the call duration estimation model.
With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the training the call duration estimation model includes:
determining an initial call duration estimation model;
acquiring a call duration training data set, wherein the call duration training data set comprises characteristic data of historical call content and call duration corresponding to the historical call;
and training the initial call duration estimation model by taking the characteristic data of the historical call content as input and taking the call duration corresponding to the historical call as output to obtain a call duration estimation model.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, and the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the present disclosure further includes:
and adding the feature data of the call content and the call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model so as to train the call duration estimation model.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, and the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the calculating a waiting duration of the queued object according to the call duration corresponding to the queued object includes:
determining a target queuing object in the current queue;
and calculating the sum of the call durations corresponding to one or more queuing objects positioned in front of the target queuing object in the current queue, and determining the sum of the call durations as the waiting duration of the target queuing object.
With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, the fourth implementation manner of the first aspect, and the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the prompting, based on the waiting duration of the queued object, of the queued object includes:
displaying the waiting time of the queuing object; or generating a prompt message of the waiting duration based on the waiting duration of the queuing object, and sending the prompt message of the waiting duration to the queuing object.
In a second aspect, an information prompting device is provided in the embodiments of the present disclosure.
Specifically, the information prompt apparatus includes:
the system comprises an acquisition module, a queue management module and a queue management module, wherein the acquisition module is configured to determine one or more queuing objects in a current queue and acquire conversation contents between a preset conversation object and the queuing objects;
the estimation module is configured to estimate the call duration corresponding to the queuing object according to the call content;
and the calculation module is configured to calculate the waiting time of the queuing object according to the call time corresponding to the queuing object, and prompt the queuing object based on the waiting time of the queuing object.
With reference to the second aspect, in a first implementation manner of the second aspect, the estimating module is configured to:
acquiring the characteristic data of the call content, wherein the characteristic data of the call content comprises one or more of the following data: call category, call keyword;
and inputting the characteristic data of the call content into a pre-trained call duration estimation model to obtain the call duration corresponding to the queuing object.
With reference to the second aspect and the first implementation manner of the second aspect, in a second implementation manner of the second aspect, an embodiment of the present disclosure further includes:
a training module configured to train the call duration estimation model.
With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in a third implementation manner of the second aspect, the training module is configured to:
determining an initial call duration estimation model;
acquiring a call duration training data set, wherein the call duration training data set comprises characteristic data of historical call content and call duration corresponding to the historical call;
and training the initial call duration estimation model by taking the characteristic data of the historical call content as input and taking the call duration corresponding to the historical call as output to obtain a call duration estimation model.
With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, and the third implementation manner of the second aspect, in a fourth implementation manner of the second aspect, the training module is further configured to:
and adding the feature data of the call content and the call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model so as to train the call duration estimation model.
With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, and the fourth implementation manner of the second aspect, in a fifth implementation manner of the second aspect of the present disclosure, the part of the calculating module, which calculates the waiting duration of the queued object according to the call duration corresponding to the queued object, is configured to:
determining a target queuing object in the current queue;
and calculating the sum of the call durations corresponding to one or more queuing objects positioned in front of the target queuing object in the current queue, and determining the sum of the call durations as the waiting duration of the target queuing object.
With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, the fourth implementation manner of the second aspect, and the fifth implementation manner of the second aspect, in a sixth implementation manner of the second aspect, the portion of the calculation module that prompts the queued object based on the waiting duration of the queued object is configured to:
displaying the waiting time of the queuing object; or generating a prompt message of the waiting duration based on the waiting duration of the queuing object, and sending the prompt message of the waiting duration to the queuing object.
In a third aspect, the disclosed embodiments provide an electronic device, including a memory for storing one or more computer instructions that support an information prompting apparatus to execute the above information prompting method, and a processor configured to execute the computer instructions stored in the memory. The information prompting device can also comprise a communication interface used for communicating with other equipment or a communication network.
In a fourth aspect, the disclosed embodiments provide a computer-readable storage medium for storing computer instructions for an information presentation apparatus, which includes computer instructions for performing the above-mentioned information presentation method as an information presentation apparatus.
In a fifth aspect, the disclosed embodiments provide a computer program product comprising a computer program/instructions, which when executed by a processor, implement the steps of the above-mentioned information prompting method.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the technical scheme, the communication with the queuing objects in the queuing queue is carried out in advance, the corresponding communication time length is estimated according to the communication content, and the waiting time length of each queuing object is calculated and obtained based on the estimated communication time length. According to the technical scheme, the user can be accurately prompted about how long the user needs to wait, and the user experience and the platform service quality are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of embodiments of the disclosure.
Drawings
Other features, objects, and advantages of embodiments of the disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 illustrates a flow diagram of an information prompting method according to an embodiment of the present disclosure;
fig. 2 is a block diagram showing a configuration of an information presentation apparatus according to an embodiment of the present disclosure;
FIG. 3 shows a block diagram of an electronic device according to an embodiment of the present disclosure;
FIG. 4 is a schematic block diagram of a computer system suitable for implementing an information prompting method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the disclosed embodiments will be described in detail with reference to the accompanying drawings so that they can be easily implemented by those skilled in the art. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the disclosed embodiments, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
According to the technical scheme provided by the embodiment of the disclosure, a call is carried out with the queuing objects in the queuing queue in advance, the corresponding call duration is estimated according to the call content, and the waiting duration of each queuing object is calculated and obtained based on the estimated call duration. According to the technical scheme, the user can be accurately prompted about how long the user needs to wait, and the user experience and the platform service quality are improved.
Fig. 1 shows a flowchart of an information presentation method according to an embodiment of the present disclosure, and as shown in fig. 1, the information presentation method includes the following steps S101 to S103:
in step S101, determining one or more queuing objects in a current queue, and acquiring call content between a preset call object and the queuing object;
in step S102, a call duration corresponding to the queuing object is estimated according to the call content;
in step S103, the waiting duration of the queued object is calculated according to the call duration corresponding to the queued object, and the queued object is prompted based on the waiting duration of the queued object.
As mentioned above, with the development of science and technology, users seek services for various platforms through networks, phones, and other channels, and service providers often communicate with users through networks or phones to receive user's consultation, obtain user's needs, solve user's problems, and provide help for users. In practical application, a user requesting manual customer service generally needs to wait in a queue, while the prior art generally only can prompt the user that a few people in front of the user wait, or prompt the user to queue the second place of the waiting queue, and cannot relatively accurately prompt the user how long the user needs to wait. This is not conducive to improving the user experience and improving the quality of service of the platform.
In view of the above problem, in this embodiment, an information prompting method is provided, in which a call is performed with a queuing object in a queuing queue in advance, a corresponding call duration is estimated according to call contents, and a waiting duration of each queuing object is calculated based on the estimated call duration. According to the technical scheme, the user can be accurately prompted about how long the user needs to wait, and the user experience and the platform service quality are improved.
In an embodiment of the present disclosure, the information presenting method may be applied to an information presenter who presents information, such as a computer, an electronic device, and a server.
In an embodiment of the present disclosure, the queue refers to a queue waiting for processing by an artificial customer service seat, one or more queuing objects exist in the queue, and when there is no queuing object before a certain queuing object, the queuing object can achieve the purpose of communicating with the artificial customer service seat.
In an embodiment of the present disclosure, the preset call object refers to an object that has made a call with the queuing object before the queuing object formally starts a call with the human service seat when the queuing object is in queue, so that a call duration required after the queuing object formally starts a call with the human service seat can be estimated by using a call content between the queuing object and the preset call object. The preset call object can be a manual call object similar to a manual customer service seat, or can be an object such as a call robot.
In an embodiment of the present disclosure, the call refers to a call realized by both parties of a call object based on a data network or a communication network, and in this embodiment, the call includes not only a voice call but also a text conversation.
In an embodiment of the present disclosure, the call content is obtained during a call.
In an embodiment of the present disclosure, the call duration corresponding to the queuing object refers to a call duration that may be continued after the queuing object formally starts a call with the human service seat, which is estimated according to the call content between the preset call object and the queuing object, and the call durations corresponding to different queuing objects are likely to be different.
In an embodiment of the present disclosure, the waiting time of a queuing object refers to a waiting time required for a certain queuing object to formally start talking with an artificial customer service seat from joining the queuing object to the queuing object.
In the above embodiment, after the current queuing queue is obtained, first, one or more queuing objects in the current queuing queue are determined, a call between a preset call object and each queuing object is established, and call content between the preset call object and each queuing object is obtained; then, according to the communication content between the preset communication object and each queuing object, estimating and obtaining the communication time length which may be continued after the queuing object formally starts to communicate with the manual service seat, namely the communication time length corresponding to the queuing object; and then, calculating the waiting time required by the queuing object according to the estimated call time corresponding to the queuing object, and prompting the queuing object based on the waiting time of the queuing object.
In an embodiment of the present disclosure, the step S102, namely, predicting the call duration corresponding to the queuing object according to the call content, may include the following steps:
acquiring the characteristic data of the call content, wherein the characteristic data of the call content comprises one or more of the following data: a call category, a call keyword;
and inputting the characteristic data of the call content into a pre-trained call duration estimation model to obtain the call duration corresponding to the queuing object.
In this embodiment, a pre-trained call duration estimation model is used to estimate the call duration corresponding to the queuing object, that is, feature data of the call content is first obtained, where the feature data of the call content may include one or more of the following data: call categories, call keywords, and the like, where the call categories may be pre-sale consultation, post-sale consultation, logistics consultation, invoice service, transaction dispute, account service, complaint right, other consultation, and the like, and of course, the call categories are related to the fields to which the calls belong, and the call categories corresponding to different fields may be different; the call keywords refer to keywords extracted from the call content, wherein the call category and the call keywords can be obtained based on the call content by means of semantic analysis; considering that the call category and the call keyword are associated with the call duration, and the call category and the call keyword are different, the call duration may be different, and therefore, the call category and the call keyword may be selected as the feature data of the call content, and of course, a person skilled in the art may also select other feature data associated with the call duration according to the needs of practical applications, and the present disclosure does not particularly limit the feature data of the call content. And then inputting the obtained feature data of the call content into a pre-trained call duration estimation model, so as to obtain the call duration which is possibly continued after the queuing object formally starts to call with the manual customer service seat, namely the call duration corresponding to the queuing object.
The estimation of the call duration can be carried out after the call between the preset call object and the queuing object is completed. Considering that the information possibly provided by the queuing object is not comprehensive enough in the process of the conversation with the preset conversation object, the conversation time estimated based on the conversation content is not accurate enough, and the accuracy of the waiting time is further influenced. Therefore, in an embodiment of the present disclosure, the preset call object may further make a call with the queuing object one or more times to obtain more comprehensive call content, so as to improve the accuracy of estimating the call duration and the accuracy of calculating the waiting duration. That is, in an embodiment of the present disclosure, the step of acquiring the call content between the preset call object and the queued object in step S101 may be implemented as:
and acquiring one or more times of call contents between a preset call object and the queuing object.
In this embodiment, the step S102, namely, the step of predicting the call duration corresponding to the queuing object according to the call content, may be implemented as follows:
and estimating the call duration corresponding to the queuing object according to the one or more call contents.
In an embodiment of the present disclosure, the method may further include the steps of:
and training the call duration estimation model.
In this embodiment, the step of training the call duration estimation model may include the following steps:
determining an initial call duration estimation model;
acquiring a call duration training data set, wherein the call duration training data set comprises characteristic data of historical call content and call duration corresponding to the historical call;
and training the initial call duration estimation model by taking the characteristic data of the historical call content as input and taking the call duration corresponding to the historical call as output to obtain a call duration estimation model.
In this embodiment, when training the call duration estimation model, an initial call duration estimation model is first determined, where the initial call duration estimation model may be selected according to the needs of the actual application; then, acquiring characteristic data of historical call content and call duration corresponding to the historical call, wherein the characteristic data of the historical call content can be consistent with the characteristic data of the call content described above; then, the characteristic data of the historical call content is used as input, the call duration corresponding to the historical call is used as output to train the initial call duration estimation model, and the call duration estimation model can be obtained when the training result is converged.
In an embodiment of the present disclosure, the method may further include the steps of:
and adding the feature data of the call content and the call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model so as to train the call duration estimation model.
In order to improve the completeness of the call duration training data set as the training data of the call duration estimation model and ensure the comprehensiveness of the learning training result of the call duration estimation, in this embodiment, a feedback mechanism is used to perform the call duration estimation, namely, after the call duration estimation result is obtained by using the call duration estimation model based on the feature data of the call content obtained currently, the feature data of the call content is also obtained, and adding the obtained call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model, the talk length estimation model may subsequently be trained based on a new set of talk length training data, the accuracy of the call duration estimation is improved by enriching the training data, and a more complete call duration estimation model is obtained to participate in the output of the next call duration estimation result.
In an embodiment of the present disclosure, the step of calculating the waiting duration of the queued object according to the call duration corresponding to the queued object in step S103 may include the following steps:
determining a target queuing object in the current queue;
and calculating the sum of the call durations corresponding to one or more queuing objects positioned in front of the target queuing object in the current queue, and determining the sum of the call durations as the waiting duration of the target queuing object.
In this embodiment, the waiting duration of each queuing object in the queue may be calculated according to the estimated call duration corresponding to the queuing object. Specifically, a certain target queuing object in the current queue is determined firstly; and then calculating the sum of the call durations corresponding to one or more queuing objects in the current queue and before the target queuing object, and finally determining the sum of the call durations as the waiting duration of the target queuing object. For example, if there are 6 queuing objects in a queue, it is now desired to calculate the waiting time of the queuing object at the 6 th position in the queue, that is, the queuing object at the 6 th position in the queue is the target queuing object, and it is assumed that the estimated call times corresponding to the queuing object at the first 5 positions in the queue are 2 minutes, 3 minutes, 30 seconds, 5 minutes, 20 seconds, and 4 minutes, 50 seconds, respectively, so that the waiting time of the target queuing object is the sum of the call times corresponding to the queuing objects at the first 5 positions, and 18 minutes is 40 seconds.
In an embodiment of the present disclosure, the step of prompting the queued object based on the waiting duration of the queued object in step S103 may include the following steps:
displaying the waiting time of the queuing object; or generating a prompt message of the waiting duration based on the waiting duration of the queuing object, and sending the prompt message of the waiting duration to the queuing object.
In this embodiment, the queued object may be prompted based on the waiting duration of the queued object, for example, the calculated waiting duration of the queued object may be displayed on a corresponding display interface, for example, a queued object display interface, or a waiting duration prompt message may be generated based on the waiting duration of the queued object, and the waiting duration prompt message may be sent to the queued object.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
Fig. 2 is a block diagram showing a configuration of an information presentation apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 2, the information presentation apparatus includes:
an obtaining module 201, configured to determine one or more queuing objects in a current queue, and obtain call contents between a preset call object and the queuing object;
the estimation module 202 is configured to estimate the call duration corresponding to the queuing object according to the call content;
the calculating module 203 is configured to calculate the waiting time of the queued object according to the call time corresponding to the queued object, and prompt the queued object based on the waiting time of the queued object.
As mentioned above, with the development of science and technology, users seek services for various platforms through networks, phones, and other channels, and service providers often communicate with users through networks or phones to receive user's consultation, obtain user's needs, solve user's problems, and provide help for users. In practical application, a user requesting manual customer service generally needs to wait in a queue, while the prior art generally only can prompt the user that a few people in front of the user wait, or prompt the user to queue the second place of the waiting queue, and cannot relatively accurately prompt the user how long the user needs to wait. This is not conducive to improving the user experience and improving the quality of service of the platform.
In view of the above problem, in this embodiment, an information prompting device is provided, which performs a call with a queuing object in a queuing queue in advance, estimates a corresponding call duration according to call contents, and calculates a waiting duration of each queuing object based on the estimated call duration. According to the technical scheme, the user can be accurately prompted about how long the user needs to wait, and the user experience and the platform service quality are improved.
In one embodiment of the present disclosure, the information presentation apparatus may be implemented as an information presenter such as a computer, an electronic device, or a server that presents information.
In an embodiment of the present disclosure, the queue refers to a queue waiting for processing by an artificial customer service seat, one or more queuing objects exist in the queue, and when there is no queuing object before a certain queuing object, the queuing object can achieve the purpose of communicating with the artificial customer service seat.
In an embodiment of the present disclosure, the preset call object refers to an object that has made a call with the queuing object before the queuing object formally starts a call with the human service seat when the queuing object is in queue, so that a call duration required after the queuing object formally starts a call with the human service seat can be estimated by using a call content between the queuing object and the preset call object. The preset call object can be a manual call object similar to a manual customer service seat, or can be an object such as a call robot.
In an embodiment of the present disclosure, the call refers to a call realized by both parties of a call object based on a data network or a communication network, and in this embodiment, the call includes not only a voice call but also a text conversation.
In an embodiment of the present disclosure, the call content is obtained during a call.
In an embodiment of the present disclosure, the call duration corresponding to the queuing object refers to a call duration that may be continued after the queuing object formally starts a call with the human service seat, which is estimated according to the call content between the preset call object and the queuing object, and the call durations corresponding to different queuing objects are likely to be different.
In an embodiment of the present disclosure, the waiting time of a queuing object refers to a waiting time required for a certain queuing object to formally start talking with an artificial customer service seat from joining the queuing object to the queuing object.
In the above embodiment, after the current queuing queue is obtained, first, one or more queuing objects in the current queuing queue are determined, a call between a preset call object and each queuing object is established, and call content between the preset call object and each queuing object is obtained; then, according to the communication content between the preset communication object and each queuing object, estimating and obtaining the communication time length which may be continued after the queuing object formally starts to communicate with the manual service seat, namely the communication time length corresponding to the queuing object; and then, calculating the waiting time required by the queuing object according to the estimated call time corresponding to the queuing object, and prompting the queuing object based on the waiting time of the queuing object.
In an embodiment of the present disclosure, the estimation module 202 may be configured to:
acquiring the characteristic data of the call content, wherein the characteristic data of the call content comprises one or more of the following data: a call category, a call keyword;
and inputting the characteristic data of the call content into a pre-trained call duration estimation model to obtain the call duration corresponding to the queuing object.
In this embodiment, a pre-trained call duration estimation model is used to estimate the call duration corresponding to the queuing object, that is, feature data of the call content is first obtained, where the feature data of the call content may include one or more of the following data: call categories, call keywords, and the like, where the call categories may be pre-sale consultation, post-sale consultation, logistics consultation, invoice service, transaction dispute, account service, complaint right, other consultation, and the like, and of course, the call categories are related to the fields to which the calls belong, and the call categories corresponding to different fields may be different; the call keywords refer to keywords extracted from the call content, wherein the call category and the call keywords can be obtained based on the call content by means of semantic analysis; considering that the call category and the call keyword are associated with the call duration, and the call category and the call keyword are different, the call duration may be different, and therefore, the call category and the call keyword may be selected as the feature data of the call content, and of course, a person skilled in the art may also select other feature data associated with the call duration according to the needs of practical applications, and the present disclosure does not particularly limit the feature data of the call content. And then inputting the obtained feature data of the call content into a pre-trained call duration estimation model, so as to obtain the call duration which is possibly continued after the queuing object formally starts to call with the manual customer service seat, namely the call duration corresponding to the queuing object.
The estimation of the call duration can be carried out after the call between the preset call object and the queuing object is completed. Considering that the information possibly provided by the queuing object is not comprehensive enough in the process of the conversation with the preset conversation object, the conversation time estimated based on the conversation content is not accurate enough, and the accuracy of the waiting time is further influenced. Therefore, in an embodiment of the present disclosure, the preset call object may further make a call with the queuing object one or more times to obtain more comprehensive call content, so as to improve the accuracy of estimating the call duration and the accuracy of calculating the waiting duration. That is, in an embodiment of the present disclosure, the portion of the obtaining module 201, which obtains the call content between the preset call object and the queuing object, may be configured to:
and acquiring one or more times of call contents between a preset call object and the queuing object.
In this embodiment, the estimation module 202 may be configured to:
and estimating the call duration corresponding to the queuing object according to the one or more call contents.
In an embodiment of the present disclosure, the apparatus may further include:
a training module configured to train the call duration estimation model.
In this embodiment, the training module may be configured to:
determining an initial call duration estimation model;
acquiring a call duration training data set, wherein the call duration training data set comprises characteristic data of historical call content and call duration corresponding to the historical call;
and training the initial call duration estimation model by taking the characteristic data of the historical call content as input and taking the call duration corresponding to the historical call as output to obtain a call duration estimation model.
In this embodiment, when training the call duration estimation model, an initial call duration estimation model is first determined, where the initial call duration estimation model may be selected according to the needs of the actual application; then, acquiring characteristic data of historical call content and call duration corresponding to the historical call, wherein the characteristic data of the historical call content can be consistent with the characteristic data of the call content described above; then, the characteristic data of the historical call content is used as input, the call duration corresponding to the historical call is used as output to train the initial call duration estimation model, and the call duration estimation model can be obtained when the training result is converged.
In an embodiment of the present disclosure, the training module may be further configured to:
and adding the feature data of the call content and the call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model so as to train the call duration estimation model.
In order to improve the completeness of the call duration training data set as the training data of the call duration estimation model and ensure the comprehensiveness of the learning training result of the call duration estimation, in this embodiment, a feedback mechanism is used to perform the call duration estimation, namely, after the call duration estimation result is obtained by using the call duration estimation model based on the feature data of the call content obtained currently, the feature data of the call content is also obtained, and adding the obtained call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model, the talk length estimation model may subsequently be trained based on a new set of talk length training data, the accuracy of the call duration estimation is improved by enriching the training data, and a more complete call duration estimation model is obtained to participate in the output of the next call duration estimation result.
In an embodiment of the present disclosure, the part of the calculating module 203 that calculates the waiting duration of the queued object according to the call duration corresponding to the queued object may be configured to:
determining a target queuing object in the current queue;
and calculating the sum of the call durations corresponding to one or more queuing objects positioned in front of the target queuing object in the current queue, and determining the sum of the call durations as the waiting duration of the target queuing object.
In this embodiment, the waiting duration of each queuing object in the queue may be calculated according to the estimated call duration corresponding to the queuing object. Specifically, a certain target queuing object in the current queue is determined firstly; and then calculating the sum of the call durations corresponding to one or more queuing objects in the current queue and before the target queuing object, and finally determining the sum of the call durations as the waiting duration of the target queuing object. For example, if there are 6 queuing objects in a queue, it is now desired to calculate the waiting time of the queuing object at the 6 th position in the queue, that is, the queuing object at the 6 th position in the queue is the target queuing object, and it is assumed that the estimated call times corresponding to the queuing object at the first 5 positions in the queue are 2 minutes, 3 minutes, 30 seconds, 5 minutes, 20 seconds, and 4 minutes, 50 seconds, respectively, so that the waiting time of the target queuing object is the sum of the call times corresponding to the queuing objects at the first 5 positions, and 18 minutes is 40 seconds.
In an embodiment of the present disclosure, the portion of the calculation module 203 that prompts the queued object based on the waiting duration of the queued object may be configured to:
displaying the waiting time of the queuing object; or generating a prompt message of the waiting duration based on the waiting duration of the queuing object, and sending the prompt message of the waiting duration to the queuing object.
In this embodiment, the queued object may be prompted based on the waiting duration of the queued object, for example, the calculated waiting duration of the queued object may be displayed on a corresponding display interface, for example, a queued object display interface, or a waiting duration prompt message may be generated based on the waiting duration of the queued object, and the waiting duration prompt message may be sent to the queued object.
The present disclosure also discloses an electronic device, fig. 3 shows a block diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 3, the electronic device 300 includes a memory 301 and a processor 302; wherein,
the memory 301 is used to store one or more computer instructions, which are executed by the processor 302 to implement the above-described method steps.
FIG. 4 is a schematic block diagram of a computer system suitable for use in implementing an information prompt in accordance with an embodiment of the present disclosure.
As shown in fig. 4, the computer system 400 includes a processing unit 401 that can execute various processes in the above-described embodiments according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the system 400 are also stored. The processing unit 401, the ROM402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary. The processing unit 401 may be implemented as a CPU, a GPU, a TPU, an FPGA, an NPU, or other processing units.
In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the information prompting method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411.
A computer program product is also disclosed in embodiments of the present disclosure, the computer program product comprising computer programs/instructions which, when executed by a processor, implement any of the above method steps.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the disclosed embodiment also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the foregoing embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the embodiments of the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. An information prompting method comprises the following steps:
determining one or more queuing objects in a current queue, and acquiring conversation contents between a preset conversation object and the queuing objects;
estimating the call duration corresponding to the queuing object according to the call content;
and calculating the waiting time of the queuing object according to the call time corresponding to the queuing object, and prompting the queuing object based on the waiting time of the queuing object.
2. The method according to claim 1, wherein said predicting a call duration corresponding to the queued object according to the call content comprises:
acquiring the characteristic data of the call content, wherein the characteristic data of the call content comprises one or more of the following data: call category, call keyword;
and inputting the characteristic data of the call content into a pre-trained call duration estimation model to obtain the call duration corresponding to the queuing object.
3. The method of claim 2, further comprising:
and training the call duration estimation model.
4. The method of claim 3, the training the call duration estimation model, comprising:
determining an initial call duration estimation model;
acquiring a call duration training data set, wherein the call duration training data set comprises characteristic data of historical call content and call duration corresponding to the historical call;
and training the initial call duration estimation model by taking the characteristic data of the historical call content as input and taking the call duration corresponding to the historical call as output to obtain a call duration estimation model.
5. The method of claim 4, further comprising:
and adding the feature data of the call content and the call duration corresponding to the call as new training data into a call duration training data set of the call duration estimation model so as to train the call duration estimation model.
6. The method according to any one of claims 1 to 5, wherein the calculating the waiting duration of the queued object according to the call duration corresponding to the queued object includes:
determining a target queuing object in the current queue;
and calculating the sum of the call durations corresponding to one or more queuing objects positioned in front of the target queuing object in the current queue, and determining the sum of the call durations as the waiting duration of the target queuing object.
7. The method of any of claims 1-6, the prompting the queued object based on the wait duration of the queued object, comprising:
displaying the waiting time of the queuing object; or generating a prompt message of the waiting duration based on the waiting duration of the queuing object, and sending the prompt message of the waiting duration to the queuing object.
8. An information presentation device comprising:
the system comprises an acquisition module, a queue management module and a queue management module, wherein the acquisition module is configured to determine one or more queuing objects in a current queue and acquire conversation contents between a preset conversation object and the queuing objects;
the estimation module is configured to estimate the call duration corresponding to the queuing object according to the call content;
and the calculation module is configured to calculate the waiting time of the queuing object according to the call time corresponding to the queuing object, and prompt the queuing object based on the waiting time of the queuing object.
9. An electronic device comprising a memory and a processor; wherein,
the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the steps of the method of any one of claims 1-7.
10. A computer readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, implement the steps of the method of any one of claims 1-7.
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| CN111988478A (en) * | 2020-09-02 | 2020-11-24 | 深圳壹账通智能科技有限公司 | Incoming call management method, device, server and storage medium |
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| WO2020199961A1 (en) * | 2019-03-29 | 2020-10-08 | 时时同云科技(成都)有限责任公司 | Real-time method and system for estimating restaurant wait time |
| CN111866288A (en) * | 2020-08-05 | 2020-10-30 | 中国银行股份有限公司 | Client incoming call processing method and device |
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Application publication date: 20211008 |