CN116132589A - Optimization method and system for voice feedback menu of electric power customer service system - Google Patents
Optimization method and system for voice feedback menu of electric power customer service system Download PDFInfo
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Abstract
The invention discloses a method and a system for optimizing a voice feedback menu of an electric power customer service system, wherein the method comprises the following steps: analyzing the relation between the user characteristics and the fed-back service problem categories, and establishing a user characteristic tag library; optimizing an intelligent voice service model, recognizing user voice, and converting voice signals into text signals for semantic understanding; constructing a user behavior prediction model to predict user business requirements; optimizing the manual customer service switching function, and matching the manual customer service switching function with the customer service personnel with the largest processing of corresponding business subclasses according to the user characteristic labels and the semantic analysis results. The user voice recognition is used for directly feeding back the user demands, so that the user time is saved, the utilization rate of the self-service function is improved, and the manual customer service resources are liberated. And (3) predicting user behaviors through the establishment of a user characteristic tag library, judging user requirements in advance, distributing the wiring of customer service personnel with corresponding service proficiency, displaying the wiring to the customer service wiring personnel through an interactive interface to help quickly judge the user requirements, improving the user satisfaction and reducing the complaint rate.
Description
Technical Field
The invention relates to the technical field of power operation management, in particular to a method and a system for optimizing a voice feedback menu of a power customer service system.
Background
The number of the number keys of the mobile phone generally determines the number of service contents broadcasted by an IVR system. And the current intelligent voice is queried according to the inherent sequence, and the sequence is generally defined according to the service type of the provided service. However, the user needs to listen to the key requirement of the business to be handled after listening for a period of time, which clearly wastes the time of the user and may affect the emotion of the customer.
Another relatively large disadvantage of this approach is: for complex services, only the type of service to which it belongs can be fed back. The user sometimes cannot judge which service types (summarized, categorized, and up to 9 types of service types corresponding to the number of keys) the service to be known to himself belongs to.
While most users will skip the IVR directly to use the default 0 key (go to manual). This situation causes waste of IVR resources. When the manual customer service is switched to be distributed according to the idle state of the customer service, the customer service personnel are required to know the handling flow of each type of service, but the handling flow is not really the same. There are other problems that result in this way of random allocation to idle service personnel causing waste of resources.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing intelligent voice service system and menu selection method have the problems of fixed sequence and limited types, waste time of users but cannot achieve good processing effect, occupy unnecessary IVR resources, and improve the optimization problem of user experience.
In order to solve the technical problems, the invention provides the following technical scheme: an optimization method of a voice feedback menu of an electric power customer service system comprises the following steps:
analyzing the relation between the user characteristics and the fed-back service problem categories, and establishing a user characteristic tag library;
optimizing an intelligent voice service model, recognizing user voice, and converting voice signals into text signals for semantic understanding;
constructing a user behavior prediction model to predict user business requirements;
optimizing the manual customer service switching function, and matching the manual customer service switching function with the customer service personnel with the largest processing of corresponding business subclasses according to the user characteristic labels and the semantic analysis results.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the step of establishing the user characteristic tag library comprises the following steps:
setting basic classification of labels according to the service requirements of the power system and the actual conditions of the power industry;
setting definition and attribute requirements for label types, and performing secondary label setting;
evaluating tag definition and attribute uniqueness, integrity and rationality indexes, and ensuring the validity of the tag;
and updating the user characteristic tag library according to the tag evaluation result, and replacing or deleting the old tag.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the optimized intelligent voice service model comprises the steps of introducing automatic voice recognition and semantic analysis technology, utilizing an intelligent voice robot to directly converse with an incoming call user, matching business subclasses and directly reaching a corresponding service menu, and specifically comprises the following steps:
identifying a caller identification tag, and providing differentiated voice prompt and flow guidance according to the identity characteristics;
according to the user voice recognition user service complaints and directly jump to the corresponding service menu, repeated voice broadcasting and key selection are not needed, and time is saved;
if the user voice cannot be identified or the user requirement cannot be judged, further voice interaction is performed, if the user requirement cannot be judged again and again, an initial 0-9 key menu is provided for the customer to select, and meanwhile, a manual service key is provided.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the step of constructing the user behavior prediction model comprises the following steps:
sampling user data in the power service system by using a Bootstrap sampling algorithm;
constructing a random forest model by adopting a chi-square automatic cross checking algorithm;
constructing a binary decision tree through a random forest model to generate decision tree variables;
performing correlation analysis and cluster analysis on the variables to generate a user behavior predictor model;
and (3) importing the variables output by the random forest model into a logic regression model, and outputting a user behavior prediction result.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the Bootstrap sampling algorithm is expressed as:
selecting a random variable sample X= [ X ] 1 ,x 2 ,…,x n ]Obeys independent same distribution x i ～Z(x),i＝1,2,…,n
Wherein R (X, Z) is a random variable function with respect to X and Z, Z n As an empirical distribution function of sample X,
is Bootstrap estimation of θ (Z), which is a parameter of the overall distribution Z;
from Z n Extracting Bootstrap sample X' = [ X ] 1 ',x 2 ',…,x n ']Calculating statistics:
wherein R '(X', Z) n ) Is a statistic, R n Is T n Boottrap statistics, Z n 'is the empirical distribution function of the Bootstrap sample X';
repeating the steps for N times to finish Bootstrap sampling.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the Logistics regression model is expressed as:
wherein p (y=1/a) represents a user sensitivity coefficient, and the larger the value is, the greater the probability that the user has bad emotion guiding or complaints is, and careful treatment is needed; a is that i,i As m-dimensional vector argument, beta i,i Parameters to be estimated are m-dimensional vectors.
As a preferable scheme of the optimization method of the voice feedback menu of the electric power customer service system, the invention comprises the following steps: the optimized manual customer service switching function comprises the following steps: according to the user characteristic labels and the semantic analysis results of the intelligent voice service, matching customer service personnel, and according to customer service personnel skill labels and the processing quantity of business subclasses, automatically matching the customer service personnel who are good at processing the business subclasses or have the largest quantity of work orders for processing the corresponding business subclasses;
identifying whether the call is a plurality of times according to the call number of the user, if so, preferentially matching customer service personnel which are the same as the last call to answer;
and generating customer service feature images in real time according to the user feature labels and the intelligent voice service semantic analysis results, and displaying the customer service feature images to customer service personnel through an interactive interface, wherein the customer service feature images comprise user basic information, historical service information, electricity utilization feature information, abnormal states and multi-dimensional display of user behavior prediction results.
An optimization system for a voice feedback menu of an electric power customer service system, comprising: the system comprises a data acquisition and storage module, a logic calculation module, a result output module and an allocation scheduling module.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the method as described above.
The invention has the beneficial effects that: according to the optimization method and the system for the voice feedback menu of the power customer service system, provided by the invention, the user needs are directly fed back through user voice recognition, so that the user time is saved, the utilization rate of self-service functions is improved, and the manual customer service resources are liberated. And meanwhile, the user behavior prediction is carried out through the establishment of the user characteristic tag library, the user requirements are judged in advance, the wiring of customer service personnel with corresponding service proficiency is distributed, the customer service wiring personnel is displayed through an interactive interface to help quickly judge the user requirements, the user satisfaction is improved, and the complaint rate is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flowchart of an optimization method of a voice feedback menu of an electric customer service system according to a first embodiment of the present invention;
FIG. 2 is a system configuration diagram of an optimizing system for a voice feedback menu of an electric customer service system according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an internal configuration of a computer device in a computer device of a method and a system for optimizing a voice feedback menu of an electric customer service system according to a second embodiment of the present invention;
FIG. 4 is a schematic diagram of an intelligent voice navigation construction scheme of a method and a system for optimizing a voice feedback menu of an electric power customer service system according to a third embodiment of the present invention;
FIG. 5 is a flowchart of an intelligent voice service after optimizing the voice feedback menu of the electric power customer service system according to the third embodiment of the present invention;
FIG. 6 is a flowchart of a method for optimizing a voice feedback menu of an electric power customer service system and a method for constructing a user prediction model in the system according to a third embodiment of the present invention;
fig. 7 is a diagram illustrating an example of a customer service feature in a method and a system for optimizing a voice feedback menu of an electric customer service system according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a method for optimizing a voice feedback menu of an electric customer service system, including:
s1: analyzing the relation between the user characteristics and the fed-back service problem categories, and establishing a user characteristic tag library;
further, the step of establishing a user feature tag library includes:
setting basic classification of labels according to the service requirements of the power system and the actual conditions of the power industry;
setting definition and attribute requirements for label types, and performing secondary label setting;
evaluating tag definition and attribute uniqueness, integrity and rationality indexes, and ensuring the validity of the tag;
and updating the user characteristic tag library according to the tag evaluation result, and replacing or deleting the old tag.
S2: optimizing an intelligent voice service model, recognizing user voice, and converting voice signals into text signals for semantic understanding;
furthermore, an automatic voice recognition and semantic analysis technology is introduced, an intelligent voice robot is utilized to directly converse with an incoming call user, and the service subclass is matched to directly reach a corresponding service menu, and the specific steps are as follows:
identifying a caller identification tag, and providing differentiated voice prompt and flow guidance according to the identity characteristics;
according to the user voice recognition user service complaints and directly jump to the corresponding service menu, repeated voice broadcasting and key selection are not needed, and time is saved;
if the user voice cannot be identified or the user requirement cannot be judged, further voice interaction is performed, if the user requirement cannot be judged again and again, an initial 0-9 key menu is provided for the customer to select, and meanwhile, a manual service key is provided.
S3: and constructing a user behavior prediction model to predict user business requirements.
Further, the step of constructing the user behavior prediction model includes:
sampling user data in the power service system by using a Bootstrap sampling algorithm;
constructing a random forest model by adopting a chi-square automatic cross checking algorithm;
constructing a binary decision tree through a random forest model to generate decision tree variables;
performing correlation analysis and cluster analysis on the variables to generate a user behavior predictor model;
and (3) importing the variables output by the random forest model into a logic regression model, and outputting a user behavior prediction result.
Further, the Bootstrap sampling algorithm includes:
selecting a random variable sample X= [ X ] 1 ,x 2 ,…,x n ]Obeys independent same distribution x i ～Z(x),i＝1,2,…,n
Wherein R (X, Z) is a random variable function with respect to X and Z, Z n As an empirical distribution function of sample X,
is Bootstrap estimation of θ (Z), which is a parameter of the overall distribution Z;
from Z n Extracting Bootstrap sample X' = [ X ] 1 ',x 2 ',…,x n ']Calculating statistics:
wherein R '(X', Z) n ) Is a statistic, R n Is T n Boottrap statistics, Z n 'is the empirical distribution function of the Bootstrap sample X';
repeating the steps for N times to finish Bootstrap sampling.
Further, the Logistics regression model is expressed as:
wherein p (y=1/a) represents a user sensitivity coefficient, and the larger the value is, the greater the probability that the user has bad emotion guiding or complaints is, and careful treatment is needed; a is that i,i As m-dimensional vector argument, beta i,i Parameters to be estimated are m-dimensional vectors.
S4: optimizing a manual customer service switching function, and matching the manual customer service switching function with customer service personnel with the largest processing amount of corresponding business subclasses according to the user characteristic labels and the semantic analysis results;
furthermore, customer service personnel matching is performed according to the user characteristic labels and the semantic analysis results of the intelligent voice service, and customer service personnel with highest quantity of work orders good for processing the business subclasses or corresponding business subclasses are automatically matched according to the skill labels of the customer service personnel and the processing quantity of the business subclasses.
Meanwhile, whether the call is a plurality of times is identified according to the call number of the user, if so, customer service personnel which are the same as the last call are preferentially matched for answering.
Further, according to the user characteristic labels and the semantic analysis results of the intelligent voice service, client service characteristic images are generated in real time and displayed to customer service personnel through an interactive interface, and multidimensional display including user basic information, historical service information, electricity utilization characteristic information, abnormal states, user behavior prediction results and the like is performed;
wherein, the user basic information includes: archival contact information, family number information, address information, key customers (businesses), payment preferences, etc.;
the history service information includes: historical incoming call quantity, current month/March incoming call frequency, average call duration, complaint early warning level, complaint information and the like;
the electricity utilization characteristic information comprises: the electricity consumption type, the electricity consumption scale (about three days, this month, about three months), the area where the electricity consumption type is located, the area attribute, the area complaint rate and the like;
the abnormal state includes: whether arrears, whether in a power failure area, whether in a low-voltage transformer area, whether an unprocessed or processed complaint work order exists, whether electricity stealing behaviors exist, whether other anomalies exist, and the like;
and (3) predicting a user behavior prediction result, and based on the user behavior prediction model, predicting the current call-making purpose and the user sensitivity score of the user, intelligently providing a service module for pre-selection and matching with a worker with high service proficiency for docking.
Example 2
Referring to fig. 2-3, for one embodiment of the present invention, there is provided an optimization system for a voice feedback menu of an electric customer service system, including:
the data acquisition and storage module 100 is used for acquiring user data of the power system and constructing a user characteristic tag library;
the logic calculation module 200 is used for calibrating the user characteristic labels and predicting the user behaviors;
the result output module is used for generating a user portrait based on the user characteristic labels and the user behavior prediction results;
the distribution scheduling module 400 is used for accessing the intelligent voice system, transferring the voice request of the manual customer service part, calling the user portrait of the result output module 300, and distributing corresponding customer service personnel to answer.
All or part of each module in the optimization system of the voice feedback menu of the electric power customer service system can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The computer device may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data cluster data of the power monitoring system. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing an optimization method of a voice feedback menu of the electric power customer service system.
Example 3
Referring to fig. 4-7, for one embodiment of the present invention, an optimization method and system for a voice feedback menu of an electric power customer service system are provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through simulation experiments.
First, for the method and system of the above embodiments, optimization and improvement are performed based on the guangxi electric network 95598 electric service IVR system.
As in fig. 4, a scheme is constructed for intelligent voice navigation. And carrying out service carding and flow scheme formulation through demand analysis, carrying out flattening and layering treatment on the prior service knowledge, confirming service attributes and ranges, and constructing knowledge points to complete system function logic design and step prompt setting. After the basic framework is completed, script development and verification test are carried out, whether the optimization effect of the test system reaches the upper limit standard or not is tested, and the test system is applied to the existing power service IVR system for optimization.
FIG. 5 is a flowchart of the intelligent voice service after optimization. Automatic voice recognition and semantic analysis technology is introduced, and an intelligent voice robot is utilized to directly converse with an incoming call user, match business subclasses and directly reach corresponding service menus. If the user voice cannot be identified or the user requirement cannot be judged, further voice interaction is performed, if the user requirement cannot be judged again and again, an initial 0-9 key menu is provided for the customer to select, and meanwhile, a manual service key is provided.
The optimized intelligent voice service and navigation mode can directly feed back the user demands through user voice recognition, so that the user time is saved, the utilization rate of self-service functions is improved, and the manual customer service resources are liberated.
Referring to fig. 6, a user behavior prediction model is constructed according to the method steps to generate a customer service feature image shown in fig. 7, and the customer service feature image is displayed to customer service personnel through an interactive interface, so that the customer service personnel can intuitively and rapidly judge the user requirements, the user satisfaction is improved, and the complaint rate is reduced.
After the implementation of the optimization scheme is completed, the service IVR system data of the Guangxi power grid 2021 year 1-4 month 95598 after the optimization of the tracking system is compared with the service IVR system data of the previous quarter, the average duration of IVR self-service is shortened by 27.5%, the self-service to manual switching rate is reduced by 34.8%, the average duration of manual customer service is shortened by 14.8%, the service utilization rate and the service success rate of the self-service system are effectively improved, the manual resources are saved, the time cost is reduced, and the service efficiency is effectively improved.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The optimizing method of the voice feedback menu of the electric power customer service system is characterized by comprising the following steps of:
analyzing the relation between the user characteristics and the fed-back service problem categories, and establishing a user characteristic tag library;
optimizing an intelligent voice service model, recognizing user voice, and converting voice signals into text signals for semantic understanding;
constructing a user behavior prediction model to predict user business requirements;
optimizing the manual customer service switching function, and matching the manual customer service switching function with the customer service personnel with the largest processing of corresponding business subclasses according to the user characteristic labels and the semantic analysis results.
2. The method for optimizing voice feedback menus of an electric customer service system as recited in claim 1, wherein: the step of establishing the user characteristic tag library comprises the following steps:
setting basic classification of labels according to the service requirements of the power system and the actual conditions of the power industry;
setting definition and attribute requirements for label types, and performing secondary label setting;
evaluating tag definition and attribute uniqueness, integrity and rationality indexes, and ensuring the validity of the tag;
and updating the user characteristic tag library according to the tag evaluation result, and replacing or deleting the old tag.
3. The method for optimizing voice feedback menus of an electric customer service system as recited in claim 1, wherein: the optimized intelligent voice service model comprises the steps of introducing automatic voice recognition and semantic analysis technology, utilizing an intelligent voice robot to directly converse with an incoming call user, matching business subclasses and directly reaching a corresponding service menu, and specifically comprises the following steps:
identifying a caller identification tag, and providing differentiated voice prompt and flow guidance according to the identity characteristics;
according to the user voice recognition user service complaints and directly jump to the corresponding service menu, repeated voice broadcasting and key selection are not needed, and time is saved;
if the user voice cannot be identified or the user requirement cannot be judged, further voice interaction is performed, if the user requirement cannot be judged again and again, an initial 0-9 key menu is provided for the customer to select, and meanwhile, a manual service key is provided.
4. A method for optimizing voice feedback menus of an electric customer service system as claimed in claims 1, 2 and 3, wherein: the step of constructing the user behavior prediction model comprises the following steps:
sampling user data in the power service system by using a Bootstrap sampling algorithm;
constructing a random forest model by adopting a chi-square automatic cross checking algorithm;
constructing a binary decision tree through a random forest model to generate decision tree variables;
performing correlation analysis and cluster analysis on the variables to generate a user behavior predictor model;
and (3) importing the variables output by the random forest model into a logic regression model, and outputting a user behavior prediction result.
5. The method for optimizing voice feedback menus of an electric customer service system as recited in claim 4, wherein: the Bootstrap sampling algorithm is expressed as:
selecting a random variable sample X= [ X ] 1 ,x 2 ,…,x n ]Obeys independent same distribution x i ～Z(x),i＝1,2,…,n
Wherein R (X, Z) is a random variable function with respect to X and Z, Z n As an empirical distribution function of sample X,
is Bootstrap estimation of θ (Z), which is a parameter of the overall distribution Z;
from Z n Extracting Bootstrap sample X' = [ X ] 1 ',x 2 ',…,x n ']Calculating statistics:
wherein R '(X', Z) n ) Is a statistic, R n Is T n Boottrap statistics, Z n 'is the empirical distribution function of the Bootstrap sample X';
repeating the steps for N times to finish Bootstrap sampling.
6. A method for optimizing voice feedback menus of an electric customer service system as claimed in claims 4 and 5, wherein: the Logistics regression model is expressed as:
wherein p (y=1/a) represents a user sensitivity coefficient, and the larger the value is, the greater the probability that the user has bad emotion guiding or complaints is, and careful treatment is needed; a is that i,i As m-dimensional vector argument, beta i,i Parameters to be estimated are m-dimensional vectors.
7. A method for optimizing voice feedback menus of an electric customer service system as claimed in any one of claims 1-7, wherein: the optimized manual customer service switching function comprises the following steps: according to the user characteristic labels and the semantic analysis results of the intelligent voice service, matching customer service personnel, and according to customer service personnel skill labels and the processing quantity of business subclasses, automatically matching the customer service personnel who are good at processing the business subclasses or have the largest quantity of work orders for processing the corresponding business subclasses;
identifying whether the call is a plurality of times according to the call number of the user, if so, preferentially matching customer service personnel which are the same as the last call to answer;
and generating customer service feature images in real time according to the user feature labels and the intelligent voice service semantic analysis results, and displaying the customer service feature images to customer service personnel through an interactive interface, wherein the customer service feature images comprise user basic information, historical service information, electricity utilization feature information, abnormal states and multi-dimensional display of user behavior prediction results.
8. An optimization system for a voice feedback menu of an electric power customer service system, comprising: the system comprises a data acquisition and storage module (100), a logic calculation module (200), a result output module (300) and an allocation scheduling module (400).
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117059095A (en) * | 2023-07-21 | 2023-11-14 | 广州市睿翔通信科技有限公司 | IVR-based service providing method and device, computer equipment and storage medium |
| CN118396551A (en) * | 2024-04-24 | 2024-07-26 | 北京中天瑞合科技有限公司 | AI intelligent office application platform for college service |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117059095A (en) * | 2023-07-21 | 2023-11-14 | 广州市睿翔通信科技有限公司 | IVR-based service providing method and device, computer equipment and storage medium |
| CN117059095B (en) * | 2023-07-21 | 2024-04-30 | 广州市睿翔通信科技有限公司 | IVR-based service providing method and device, computer equipment and storage medium |
| CN118396551A (en) * | 2024-04-24 | 2024-07-26 | 北京中天瑞合科技有限公司 | AI intelligent office application platform for college service |
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