CN113645364B - Intelligent voice outbound method for power dispatching - Google Patents
Intelligent voice outbound method for power dispatching Download PDFInfo
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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
- 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
- H04M3/5232—Call distribution algorithms
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- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
- G10L15/063—Training
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/60—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for measuring the quality of voice signals
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/63—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for estimating an emotional state
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
The application provides an intelligent voice outbound method facing power dispatching, which comprises the following steps of preprocessing voice information: collecting voice matching content to form a voice signal, identifying service appeal of a user in real time, and preprocessing the voice signal to obtain a text character string S; judging whether VIP users, people with weak self-help ability, emergency repeated incoming calls and the like are required, if so, manual and intelligent voice interaction understanding is needed, multiple-round interaction understanding functions are supported, text character strings S automatically fill grooves and ask backwards in the interaction process, and users are guided to complement information S n Decoding according to the semantic code C, the set emotion representation Con (Q) and the set theme representation Sof (Q) to obtain a target sequence Y; and checking the quality of the outbound result, and checking the service completion condition with an outbound service system. The method can effectively and accurately connect the service completion condition with the outbound service system, provide continuous optimization service of the telephone operation model for power dispatching, and save maintenance cost of the telephone operation model for power dispatching.
Description
Technical Field
The application relates to the field of voice service, in particular to an intelligent voice outbound method facing power dispatching.
Background
The intelligent voice-based navigation system is intelligent, and has important significance for intelligent upgrading of dispatcher business operations such as ground dispatching operation, fault handling, signal monitoring and the like in current power grid dispatching. The development of artificial intelligence technology is mature day by day, and the system has strong universality, standardized, automatic and modularized industrial mass production characteristics and has the capability of deeply enabling power dispatching. The application of the artificial intelligence technology depends on the continuous maintenance of manpower, the good intelligent outbound service effect can be achieved only by the continuous tuning of the conversation model by the organization professional, and the personnel investment of most institutions in this aspect is difficult to ensure, so that the intelligent outbound service effect is affected. Traditional customer service system excessively relies on the manpower mode, needs to reduce power dispatching input cost, provides the continuous optimization service of conversation model for power dispatching, saves the conversation model maintenance cost of power dispatching.
Disclosure of Invention
The embodiment of the application provides an intelligent voice outbound method for power dispatching, which can effectively and accurately dock the service completion condition with an outbound service system, provide continuous optimization service of a telephone model for power dispatching, and save the maintenance cost of the telephone model for power dispatching.
The method specifically comprises the following steps:
step one, preprocessing voice information: collecting voice matching content to form a voice signal, identifying service appeal of a user in real time, and preprocessing the voice signal to obtain a text character string S;
step two, intelligent prejudging and transferring are carried out manually, and whether VIP users, people with weak self-help ability, emergency repeated incoming calls and the like are judged according to the text character string S in the step one, if so, the step eight is directly transferred, and if not, the text character string S is transferred to the step three;
step four, intelligent voice interactive understanding, supporting a multi-round interactive understanding function, automatically filling grooves and asking back in the text character string S in the interactive process, and guiding a user to complement information S n The context information is associated to accurately acquire a user demand character string Q;
mapping the source sequence intelligent voice interaction understanding model U into a semantic vector C, using a cyclic neural network as a decoder, and decoding to obtain a target sequence Y according to the semantic code C, the set emotion representation Con (Q) and the set theme representation Sof (Q);
step six, the quality inspection of the outbound result, check the service completion situation with the outbound business system, whether to finish effectively, transfer the result into step seven and step eight;
and step seven, model retraining is carried out by updating the model, the result of the step six is fed back to the step five in real time, the intelligent outbound model is updated, model parameters are trained, and the model is updated.
Optionally, the fourth step includes:
the text character string S automatically fills the slot for back inquiry in the interaction process, guides the user to complement the information Sn, and correlates the context information to accurately acquire the character string Q required by the user,
Q＝{S,S 1 ,...,S n }；
wherein Q is a user demand string, S 1 ,...,S n The method is that intelligent voice interaction understanding complement each information character string;
the intelligent voice interaction understanding model U comprises voice emotion recognition and semantic emotion recognition;
U＝∑Con(Q)+Sof(Q)；
wherein Con (Q) is speech emotion recognition; judging the emotion of the customer according to the threshold value of the speed and the severity of the language of the customer; sof (Q) is semantic emotion recognition; judging the dissatisfaction of the client according to the keywords of the sentences of the client, such as the keywords of the dirty words, complaints and the like;
if the emotion of the customer continuously fluctuates, the demand understanding information U is transmitted to the fifth step.
Optionally, the fifth step includes:
mapping the source sequence intelligent voice interaction understanding model U into a semantic vector C, using a cyclic neural network (Recurrent Neural Network, RNN) as a decoder, and decoding to obtain a target sequence Y according to the semantic code C, the set emotion representation Con (Q) and the set theme representation Sof (Q);
C＝f(Q 1 ,Q 2 ,...,Q M )；
the target sequence Y supports the butt joint of third party service systems such as a dispatching system and the like, realizes the extraction of key information in the interaction process, and is pushed to the third party service system through an interface, and the outbound result condition is fed back to the step six.
Optionally, the method includes:
and step eight, artificial service deficiency.
The beneficial effects are that:
the method can effectively and accurately connect the service completion condition with the outbound service system, provide continuous optimization service of the telephone operation model for power dispatching, and save maintenance cost of the telephone operation model for power dispatching.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an intelligent voice outbound method for power dispatching.
Detailed Description
In order to make the structure and advantages of the present application more apparent, the structure of the present application will be further described with reference to the accompanying drawings.
Referring to fig. 1, the application discloses an intelligent voice outbound method facing power dispatching, which comprises the following steps:
step one, preprocessing voice information: collecting voice matching content to form a voice signal, identifying service appeal of a user in real time, and preprocessing the voice signal to obtain a text character string S;
step two, intelligent prejudging and transferring are carried out manually, and whether VIP users, people with weak self-help ability, emergency repeated incoming calls and the like are judged according to the text character string S in the step one, if so, the step eight is directly transferred, and if not, the text character string S is transferred to the step three;
step three, intelligent prejudging and switching keys, wherein step two does not need to switch to manual service, and the step four is further judged whether to switch to key navigation service or not according to database matching, knowledge point matching, speaking matching, keyword matching and the like when the maintenance task is configured in the text character string S by combining number source and interface source information;
step four, intelligent voice interactive understanding, supporting a multi-round interactive understanding function, automatically filling grooves and asking back in the text character string S in the interactive process, and guiding a user to complement information S n And the context information is associated to accurately acquire the user demand character string Q,
Q＝{S,S 1 ,...,S n }；
wherein Q is a user demand string, S 1 ,...,S n Is intelligent voice interactive understanding complement information character strings.
The intelligent voice interaction understanding model U comprises voice emotion recognition and semantic emotion recognition.
U＝∑Con(Q)+Sof(Q)；
Wherein Con (Q) is speech emotion recognition. And judging the emotion of the customer according to the threshold value of the voice intensity and the voice speed of the customer. Sof (Q) is semantic emotion recognition. Judging the dissatisfaction of the client according to the keywords of the sentences of the client, such as the keywords of the dirty words, complaints and the like
The intelligent speech interaction understanding model U is identified as pleasant, general, discontent, anger. And identifying the emotion state of the client according to the emotion service strategy model, and reminding intervention pacifying in real time. If the emotion of the customer continuously fluctuates, a team leader is warned in time, and meanwhile, the customer emotion is properly strategically compensated and calmed according to the anger emotion service strategy of the customer, so that the complaint risk is reduced. Transmitting the demand understanding information U into a fifth step;
step five, calculating an intelligent outbound model,
mapping the source sequence intelligent voice interaction understanding model U into a semantic vector C, and decoding to obtain a target sequence Y by using a cyclic neural network (Recurrent Neural Network, RNN) as a decoder according to the semantic code C, the set emotion representation Con (Q) and the set theme representation Sof (Q).
C＝f(Q 1 ,Q 2 ,...,Q M )；
The target sequence Y supports the butt joint of third party service systems such as a dispatching system and the like, realizes the extraction of key information (such as addresses, names, application numbers and the like) in the interaction process, and pushes the key information into the third party service system through an interface, and feeds back the outbound result condition to the step six;
step six, the quality inspection of the outbound result, check the service completion situation with the outbound business system, whether to finish effectively, transfer the result into step seven and step eight;
step seven, model retraining is carried out by updating the model, the result of the step six is fed back to the step five in real time, the intelligent outbound model is updated, model parameters are trained, and the model is updated;
and step eight, artificial service deficiency.
In the application, an intelligent outbound system task monitoring module is constructed, so that the data such as the task progress, call rate and the like of voice outbound can be monitored, and a perfect task report is provided; supporting the butt joint with a third party service system, thereby acquiring outbound numbers, key information and the like from the third party system, perfecting outbound tasks and automatically initiating outbound; the system maintenance function is provided, and is mainly used for maintaining a voice library, an outbound display number, an interface, knowledge points, a speaking operation and keywords in task configuration; supporting a third party application system to call an outbound system interface so as to acquire an outbound result (success or failure); supporting the function of calling voice interruption; the system supports the functions of outbound data recording and statistical analysis, and comprises the statistics of indexes such as audio audiometry of historical conversations, total number of calls recorded, number of calls, total call duration, average call duration, completion amount, success rate and the like. Intelligent outbound interactive dialogue system, voice and semantic recognition rate is not lower than 95%: supporting the user interaction process of intelligent outbound and response is beneficial to improving the service efficiency and enabling the customer service system to be intelligent and efficient.
It should be noted that in this document, terms such as "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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The intelligent voice outbound method for power dispatching is characterized by comprising the following steps of:
step one, preprocessing voice information: collecting voice matching content to form a voice signal, identifying service appeal of a user in real time, and preprocessing the voice signal to obtain a text character string S;
step two, intelligent prejudging and transferring are carried out manually, and whether VIP users, people with weak self-help ability, emergency repeated incoming calls and the like are judged according to the text character string S in the step one, if so, the step eight is directly transferred, and if not, the text character string S is transferred to the step three;
step three, intelligent prejudging and switching keys, wherein step two does not need to switch to manual service, and the step four is further judged whether to switch to key navigation service or not according to database matching, knowledge point matching, speaking matching, keyword matching and the like when the maintenance task is configured in the text character string S by combining number source and interface source information;
step four, intelligent voice interactive understanding, supporting a multi-round interactive understanding function, automatically filling grooves and asking back in the text character string S in the interactive process, and guiding a user to complement information S n The context information is associated to accurately acquire a user demand character string Q;
mapping the source sequence intelligent voice interaction understanding model U into a semantic vector C, using a cyclic neural network as a decoder, and decoding to obtain a target sequence Y according to the semantic vector C, the set emotion representation Con (Q) and the set theme representation Sof (Q);
step six, the quality inspection of the outbound result, check the service completion situation with the outbound business system, whether to finish effectively, transfer the result into step seven and step eight;
step seven, model retraining is carried out by updating the model, the result of the step six is fed back to the step five in real time, the intelligent outbound model is updated, model parameters are trained, and the model is updated;
and step eight, artificial service deficiency.
2. The intelligent voice outbound method for power scheduling according to claim 1, wherein the fourth step comprises:
the text character string S automatically fills the slot for back inquiry in the interaction process, guides the user to complement the information Sn, and correlates the context information to accurately acquire the character string Q required by the user,
Q＝{S,S 1 ,...,S n }；
wherein Q is a user demand string, S 1 ,...,S n The method is that intelligent voice interaction understanding complement each information character string;
the intelligent voice interaction understanding model U comprises voice emotion recognition and semantic emotion recognition;
U＝∑Con(Q)+Sof(Q)；
wherein Con (Q) is speech emotion recognition; judging the emotion of the customer according to the threshold value of the speed and the severity of the language of the customer; sof (Q) is semantic emotion recognition; judging the dissatisfaction of the client according to the keywords of the sentences of the client, such as the keywords of the dirty words, complaints and the like;
if the emotion of the customer continuously fluctuates, the demand understanding information U is transmitted to the fifth step.
3. The intelligent voice outbound method for power scheduling according to claim 1, wherein the fifth step comprises:
mapping the source sequence intelligent voice interaction understanding model U into a semantic vector C, using a cyclic neural network (Recurrent Neural Network, RNN) as a decoder, and decoding to obtain a target sequence Y according to the semantic vector C and a set emotion representation Con (Q) and a theme representation Sof (Q);
C＝f(Q 1 ,Q 2 ,...,Q M )；
the target sequence Y supports the butt joint of third party service systems such as a dispatching system and the like, realizes the extraction of key information in the interaction process, and is pushed to the third party service system through an interface, and the outbound result condition is fed back to the step six.
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