CN116915905A - Method, apparatus, device, storage medium and program product for determining calling rate - Google Patents
Method, apparatus, device, storage medium and program product for determining calling rate Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/22—Arrangements for supervision, monitoring or testing
- H04M3/36—Statistical metering, e.g. recording occasions when traffic exceeds capacity of trunks
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- H—ELECTRICITY
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The application discloses an outgoing call rate determining method, an outgoing call rate determining device, a storage medium and a program product. The method comprises the following steps: the method comprises the steps of obtaining historical incoming call quantity at a first moment, inputting the historical incoming call quantity into a differential autoregressive moving average model, predicting first incoming call quantity of a next period at the first moment through the differential autoregressive moving average model, and determining a first outgoing call rate according to the first incoming call quantity. According to the embodiment of the application, the problem of higher call loss rate under the condition of increasing the existing call quantity can be solved.
Description
Technical Field
The present application relates to the field of computer application technologies, and in particular, to a method, an apparatus, a device, a storage medium, and a program product for determining an outgoing call rate.
Background
With the rapid development of customer service systems, the agents are required to execute the calling task while receiving the calling service. When executing the call-out task, the customer service system generally calculates the number of samples to be called out each time according to the seat condition under the call-out task, and then initiates call-out based on the number of samples to be called out, and then switches the seat after the user turns on the call-out.
In this way, if the number of calls is increased, there may be insufficient agents to process the call task if the number of calls cannot be decreased in time, so that in many call tasks, the agents cannot be transferred within a predetermined time after the user turns on the call, and a high call loss rate is formed.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment, a storage medium and a program product for determining an outgoing call rate, which can solve the problem of higher call loss rate under the condition of increasing the existing incoming call quantity.
In a first aspect, an embodiment of the present application provides a method for determining an exhalation rate, where the method includes:
acquiring historical incoming call quantity of a customer service system at a first moment;
inputting the historical incoming call quantity into a differential autoregressive moving average model, and predicting a first incoming call quantity of a period next to the first moment through the differential autoregressive moving average model;
and determining a first expiration rate of the next period according to the first inflow amount.
In some embodiments, before the inputting the historical incoming call quantity into the differential autoregressive moving average model and predicting the first incoming call quantity by the differential autoregressive moving average model, the method further comprises:
Acquiring an autoregressive order and a moving average order of a customer service system;
determining an autoregressive model according to the historical call volume and the autoregressive order;
determining a moving average model according to the historical call-in quantity and the moving average order;
and combining the autoregressive model and the moving average model to obtain the differential autoregressive moving average model.
In some embodiments, the autoregressive model is:
the moving average model is:
the differential autoregressive moving average model is:
wherein mu 1 As a first constant term coefficient, y t1 Predicting incoming call volume for autoregressive model, E t1 Mu, as autoregressive model error value 2 As a second constant term coefficient, y t2 Predicting incoming call volume for moving average model, e t2 To move the average model error value, y t For the first incoming call quantity, μ is a constant term coefficient, p is an autoregressive order, q is a moving average order, γ i Is an autocorrelation coefficient, y t-i For incoming call volume of historic t-i time period, e t is current error value, θ i For the correlation coefficient of the moving average model, ε t-i is the error value of the t-i th period.
In some embodiments, the determining a first outgoing rate for the next time period based on the first incoming volume includes:
Acquiring the number of target agents at the first moment, the number of idle target agents at the first moment and the average service duration of the target agents in the last period, wherein the target agents comprise mixed sign agents and agents only processing incoming call services, and the mixed sign agents are agents processing both the incoming call services and the outgoing call services;
inputting the number of target agents at the first moment, the number of idle target agents at the first moment, the average service duration of the target agents in the last period and the first incoming call quantity into a first model constructed in advance to obtain a quantity of the incoming call at a second moment, wherein the second moment is any moment in the next period;
and determining the first expiration rate according to the amount to be expired.
In some embodiments, the determining a first outgoing rate for the next time period based on the first incoming volume includes:
determining a target adjusting coefficient according to a first call loss rate of a period on the customer service system;
and determining a first calling rate according to the first calling quantity and the target regulating coefficient.
In some embodiments, the determining the target adjustment coefficient according to the first call loss rate of the previous period of the customer service system includes:
Acquiring a history adjustment coefficient;
when the first call loss rate is larger than the maximum call loss rate threshold, reducing the historical adjustment coefficient according to the target seat quantity and the preset proportion to obtain the target adjustment coefficient;
and under the condition that the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold, increasing the historical adjustment coefficient according to a preset proportion according to the number of the target agents and the average idle time length of the target agents in the last period to obtain the target adjustment coefficient.
In some embodiments, the determining the target adjustment coefficient according to the first call loss rate of the previous period of the customer service system includes:
under the condition that the customer service system meets the speed regulation condition, determining a target regulation coefficient according to a first call loss rate of a previous period of the customer service system;
the rate adjustment conditions include at least one of:
the duration of starting the calling task by the customer service system is greater than or equal to a first duration threshold;
the first moment is not in the adjustment observation period;
the first call loss rate is larger than a maximum call loss rate threshold, or the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold.
In a second aspect, an embodiment of the present application provides an apparatus for determining an exhalation rate, the apparatus including:
the acquisition module is used for acquiring the historical incoming call quantity at the first moment;
the input module is used for inputting the historical incoming call quantity into a differential autoregressive moving average model, and predicting a first incoming call quantity of a period next to the first moment through the differential autoregressive moving average model;
and the determining module is used for determining a first calling rate according to the first calling quantity.
In a third aspect, an embodiment of the present application provides an apparatus for determining an outgoing call rate, the apparatus comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the exhalation rate determination method as described above.
In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement an exhalation rate determination method as above.
In a fifth aspect, embodiments of the present application provide a computer program product comprising computer program instructions which, when executed by a processor, implement an exhalation rate determination method as above.
In the application, the first incoming call quantity of the next period of the first moment is predicted through the differential autoregressive moving average model and the historical incoming call quantity, after the first incoming call quantity is predicted, the first outgoing call rate can be further determined in real time according to the first incoming call quantity, the outgoing call rate can be changed along with the change of the incoming call quantity, and when the incoming call quantity is increased, the outgoing call rate is properly reduced, so that the technical problem of higher call loss rate under the condition of the existing increased incoming call quantity is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a flow chart of an outgoing call rate determination method according to an embodiment of the present application;
FIG. 2 is a flow chart of an outgoing call rate determination method provided by another embodiment of the present application;
FIG. 3 is a flow chart of an outgoing call rate determination method provided by a further embodiment of the present application;
Fig. 4 is a schematic hardware structure of an outgoing call rate determining apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural view of an apparatus for determining an outgoing call rate according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
With the rapid development of customer service systems, the agents are required to execute the calling task while receiving the calling service. When executing the call-out task, the customer service system generally calculates the number of samples to be called out each time according to the seat condition under the call-out task, and then initiates call-out based on the number of samples to be called out, and then switches the seat after the user turns on the call-out. However, in the case of the incoming and outgoing call fusion scenario, if the number of samples to be outgoing is predicted by applying the existing method, when the incoming call volume is changed, the outgoing call volume cannot be adjusted in real time.
In this way, if the number of calls is increased, there may be insufficient agents to process the call task if the number of calls cannot be decreased in time, so that in many call tasks, the agents cannot be transferred within a predetermined time after the user turns on the call, and a high call loss rate is formed. In addition, in the case of a decrease in the amount of incoming call, if the amount of incoming call cannot be increased in time, there is a possibility that the agent has a long idle time, resulting in a low utilization rate of agent resources.
In the related art, two methods are generally adopted to solve the problems, namely, the method flexibly sets the allowed idle seat duty ratio, the method mainly depends on experience of operators, the labor cost is too high, and when the incoming call quantity is too frequently changed, the effective control is difficult; secondly, the outbound rate is fixedly adjusted according to the number of the mixed signing agents, and the method has longer hysteresis, and can also lead to slow number entering of the agents, increase the idle time of the agents and influence the utilization rate of the agents.
In particular, in order to solve the problems in the prior art, embodiments of the present application provide an outgoing call rate determining method, apparatus, device, storage medium, and program product. The following first describes an outgoing call rate determination method provided by an embodiment of the present application.
Fig. 1 is a flow chart of an outgoing call rate determination method according to an embodiment of the present application. The method comprises the following steps:
s110, acquiring the historical incoming call quantity of the customer service system at the first moment.
In this embodiment, the first time may be the current time, and the historical incoming call amount at the first time is the incoming call amount of the customer service system before the first time. Because the incoming call quantity at each moment is recorded in the customer service system, the historical incoming call quantity at the first moment can be directly read in the customer service system.
S120, inputting the historical incoming call quantity into a differential autoregressive moving average model, and predicting the first incoming call quantity of the next period of the first moment through the differential autoregressive moving average model.
A differential autoregressive moving average model (Autoregressive Integrated Moving Average model, ARIMA) may represent the relationship of the current value and the historical value, as well as the relationship of the current value and the error term. In the present embodiment, the first incoming call amount of the next period at the first time can be predicted by the differential autoregressive moving average model and the historical incoming call amount. That is, the historical incoming call amount is used as the input of the differential autoregressive moving average model, and the first incoming call amount of the next period at the first moment can be obtained. The next period of time is a period of time after the first time, and the duration of the period of time can be adjusted by the user in real time, which can be 1 minute or 30 seconds.
S130, determining a first expiration rate of the next period according to the first expiration quantity.
In this embodiment, after the first incoming call volume of the next period is predicted, the outgoing call volume corresponding to each time of the next period may be determined according to the first incoming call volume, and in the case where the number of outgoing calls in the next period is unchanged, the first outgoing call rate of the next period may be determined according to the outgoing call volume corresponding to each time of the next period and the number of outgoing calls in the next period.
According to the application, the first incoming call quantity of the next period of the first moment is predicted through the differential autoregressive moving average model and the historical incoming call quantity, after the first incoming call quantity is predicted, the first outgoing call rate can be further determined in real time according to the first incoming call quantity, so that the outgoing call rate can be changed along with the change of the incoming call quantity, and when the incoming call quantity is increased, the outgoing call rate is properly reduced, so that the technical problem of higher call loss rate under the condition of the existing increased incoming call quantity is solved.
As an alternative embodiment, to ensure accuracy of the incoming call prediction, before S120, the method further includes:
acquiring an autoregressive order and a moving average order of a customer service system;
determining an autoregressive model according to the historical call volume and the autoregressive order;
determining a moving average model according to the historical call-in quantity and the moving average order;
and combining the autoregressive model and the moving average model to obtain the differential autoregressive moving average model.
In this embodiment, the autoregressive model is used to describe the relationship between the historical value and the current value, and the autoregressive model can predict the incoming call volume based on the historical incoming call volume, and the autoregressive order is the order of the autoregressive model; the moving average model is used for describing the relation between the current value and the error term, the moving average model can predict the incoming call quantity based on the error term in the historical incoming call quantity, the moving average order is the order of the moving average model, and the autoregressive order and the moving average order can be determined according to the linear correlation and the correlation degree of the historical incoming call quantity.
After the autoregressive order is obtained, an autoregressive model can be determined according to the historical call-in quantity and the autoregressive order; after the moving average order is obtained, a moving average model may be determined from the historical call volume and the moving average order. After the autoregressive model and the moving average model are obtained, the autoregressive model, the moving average model and the difference method are combined, and then the differential autoregressive moving average model can be obtained. Wherein, the difference method is used for stabilizing the historical call-in quantity.
As shown in FIG. 2, in one embodiment, a specific method for constructing the differential autoregressive moving average model is as follows: and determining a group of autocorrelation functions ACF and a partial autocorrelation function PACF based on the acquired historical call volume, wherein the autocorrelation functions are used for describing the linear correlation between the current observed value and the past observed value in the time sequence, and the partial autocorrelation functions are used for calculating the correlation degree between two variables. Since there is a time series in the history of incoming call amounts in this embodiment, a set of autocorrelation functions and partial autocorrelation functions can be determined from the history of incoming call amounts.
After a group of autocorrelation functions and partial autocorrelation functions are determined, an ACF diagram and a PACF diagram can be drawn according to the autocorrelation functions and the partial autocorrelation functions, and the stationarity of the historical incoming call is observed through the ACF diagram and the PACF diagram, if the historical incoming call is stationary, processing is not needed, and if the historical incoming call is not stationary, the historical incoming call can be stationary through a difference method.
After the stabilization treatment is performed on the historical incoming call quantity, an autoregressive order and a moving average order can be determined according to the ACF graph and the PACF graph, an autoregressive model and a moving average model are further built, the autoregressive model and the moving average model are combined to obtain a differential autoregressive moving average model, then residual statistics values in the differential autoregressive moving average model are calculated, whether the residual statistics values are suitable or not is further checked, if the residual statistics values are suitable, the differential autoregressive moving average model is used for predicting the first incoming call quantity, and if the residual statistics values are unsuitable, the autoregressive order and the moving average order are redetermined.
In one embodiment, the autoregressive model is:
the moving average model is:
the differential autoregressive moving average model is:
wherein mu 1 As a first constant term coefficient, y t1 Predicting incoming call volume for autoregressive model, E t1 Mu, as autoregressive model error value 2 As a second constant term coefficient, y t2 Predicting incoming call volume for moving average model, e t2 To move the average model error value, y t For the first incoming call quantity, μ is a constant term coefficient, p is an autoregressive order, q is a moving average order, γ i Is an autocorrelation coefficient, y t-i E for incoming call volume of historic t-i time period t As the current error value, θ i For the correlation coefficient of the moving average model, E t-i Is the error value of the t-i th period.
By constructing the differential autoregressive moving average model in the mode, the relation between the first incoming call quantity and the historical incoming call quantity can be reflected, and the relation between the first incoming call quantity and the historical error value can be reflected, so that the accuracy of the first incoming call quantity prediction is ensured.
As an alternative embodiment, in order to improve the real-time performance and accuracy of the adjustment of the exhalation rate, S130 may include:
acquiring the number of target agents at the first moment, the number of idle target agents at the first moment and the average service duration of the target agents in the last period, wherein the target agents comprise mixed sign agents and agents only processing incoming call services, and the mixed sign agents are agents processing both the incoming call services and the outgoing call services;
inputting the number of target agents at the first moment, the number of idle target agents at the first moment, the average service duration of the target agents in the last period and the first incoming call quantity into a first model constructed in advance to obtain a quantity of the incoming call at a second moment, wherein the second moment is any moment in the next period;
And determining the first expiration rate according to the amount to be expired.
In this embodiment, the agents in the customer service system are classified into three types, one type is an agent that processes only the incoming service, one type is an agent that processes only the outgoing service, and the other type is a mixed-sign agent that processes both the incoming service and the outgoing service, and the target agents include a mixed-sign agent and an agent that processes only the incoming service. In this embodiment, the incoming call volume may be divided according to periods, and the duration of each period may be adjusted in real time by the user, where one period may include at least one period, and one period includes at least one time.
The first model is a pre-constructed model, and the first model can determine the amount of the call to be made at the second moment based on the number of the target agents at the first moment, the number of the target agents which are idle at the first moment, the average service duration of the target agents in the last period and the first call amount, and further determine the first call rate according to the amount of the call to be made.
According to the method, the first calling rate which is adjusted according to the calling quantity and the second moment can be determined based on the seat state condition and the predicted first calling quantity, namely the first calling rate can be determined according to the seat state condition and the first calling quantity, and the real-time performance and the accuracy of the calling rate adjustment are improved.
As an alternative embodiment, in order to improve the real-time performance of the adjustment of the exhalation rate, S130 may include:
determining a target adjusting coefficient according to a first call loss rate of a period on the customer service system;
and determining a first calling rate according to the first calling quantity and the target regulating coefficient.
In this embodiment, the target adjustment coefficient is a variable coefficient for adjusting the amount of the call to be made at the second time, and the actual amount of the call to be made at the second time may be obtained by adjusting the amount of the call corresponding to the target adjustment coefficient. The target adjustment coefficient can be adjusted according to the first call loss rate and the first average idle duration of the customer service system in the previous period.
If the first call loss rate of the previous period is higher, the fact that the call loss rate of the previous period is too large is indicated, so that the target adjustment coefficient can be properly adjusted down, the call loss rate is reduced by properly reducing the call loss rate of the current period; if the first average idle duration of the previous period is higher, the expiration of the previous period is too small, so that the target adjustment coefficient can be properly adjusted up, the expiration of the current period is properly increased, and the waste of seat resources is avoided.
Further, the actual call-out amount at the second time may be positively correlated with the target adjustment coefficient. In an embodiment, the actual volume of the call at the second time may be the product of the target adjustment coefficient and the volume of the call.
According to the method, the target adjustment coefficient can be determined based on the seat state of the previous period, and the first exhalation rate can be determined according to the seat state of the previous period according to the first exhalation rate adjusted by the target adjustment coefficient, so that the real-time property of exhalation rate adjustment is improved.
As an optional embodiment, to automatically adjust the target adjustment factor, the determining the target adjustment factor according to the first call loss rate of the previous period of the customer service system may include:
acquiring a history adjustment coefficient;
when the first call loss rate is larger than the maximum call loss rate threshold, reducing the historical adjustment coefficient according to the target seat quantity and the preset proportion to obtain the target adjustment coefficient;
and under the condition that the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold, increasing the historical adjustment coefficient according to a preset proportion according to the number of the target agents and the average idle time length of the target agents in the last period to obtain the target adjustment coefficient.
In this embodiment, the historical adjustment coefficient is an adjustment coefficient in a period previous to the period in which the first time is located, and the target adjustment coefficient is an adjustment coefficient obtained by adjusting the historical adjustment coefficient according to the first call loss rate of the previous period and the first average idle time of the previous period.
If the first call loss rate is larger than the maximum threshold value of the call loss rate, the call loss rate caused by the call loss amount of the previous period is too high, so that the history adjustment coefficient can be reduced according to the preset proportion, the call loss amount of the period can be properly reduced, and the call loss rate can be reduced; if the first call loss rate of the previous period is smaller than the minimum call loss rate threshold and the first average idle time length is larger than the average idle time length threshold, the fact that the waste of seat resources caused by the calling quantity of the previous period is overlarge is indicated, so that the history adjustment coefficient can be increased according to a preset proportion, the calling quantity of the period is increased appropriately, and the waste of the seat resources is avoided.
The preset proportion in the method can be set by a user and adjusted in real time, the historical adjustment coefficient is adjusted in real time through the preset proportion, and the target adjustment coefficient can be automatically adjusted according to the state of the target seat in the previous period.
As an optional embodiment, the determining the target adjustment coefficient according to the first call loss rate of the previous period of the customer service system may include:
under the condition that the customer service system meets the speed regulation condition, determining a target regulation coefficient according to a first call loss rate of a previous period of the customer service system;
The rate adjustment conditions include at least one of:
the duration of starting the calling task by the customer service system is greater than or equal to a first duration threshold;
the first moment is not in the adjustment observation period;
the first call loss rate is larger than a maximum call loss rate threshold, or the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold.
In this embodiment, the target adjustment coefficient is adjusted only if the customer service system satisfies the rate adjustment condition; otherwise, the obtained historical adjustment coefficient can be directly determined as the target adjustment coefficient.
In one embodiment, the customer service system can be considered to satisfy the rate adjustment conditions only if the customer service system satisfies the three rate adjustment conditions simultaneously. In another embodiment, the customer service system may be considered to satisfy the rate adjustment condition when the customer service system satisfies any one of the three above.
As shown in fig. 3, in an embodiment, the history adjustment coefficient is 1, and in the case that the customer service system satisfies the rate adjustment condition, the target adjustment coefficient is adjusted according to the acquired real-time index, and the actual call volume of the outbound is calculated according to the target adjustment coefficient; under the condition that the customer service system does not meet the rate adjustment condition, the historical adjustment coefficient is directly used as a target adjustment coefficient to calculate the actual call quantity of the outbound. And further real-time monitoring real-time indicators affecting the target adjustment coefficients.
Specifically, when the duration of the customer service system for starting the call-out task is smaller than the first time threshold, the condition of the target agent is unstable, and the first call loss rate and the first average idle duration of the previous period cannot accurately reflect the state of the target agent of the previous period, so that the duration of the customer service system for starting the call-out task is larger than or equal to the first time threshold as one of the rate adjustment conditions.
The adjusting and observing period is a period in which the customer service system is in a working state and the target adjusting coefficient can be adjusted in real time, and when the first moment is not in the adjusting and observing period, the target adjusting coefficient does not need to be adjusted in time, so that the first moment is not in the adjusting and observing period and can be used as one of rate adjusting conditions.
When the first call loss rate is smaller than or equal to the maximum threshold value of the call loss rate and is larger than or equal to the minimum threshold value of the call loss rate, the target seat of the customer service system in the previous period is in a proper working state interval, and therefore the call loss rate is not too high due to too high call loss rate, and seat resource waste is not too high due to too low call loss rate, and the target adjustment coefficient is not required to be adjusted under the condition. When the first call loss rate is larger than the maximum threshold value of the call loss rate, the call loss rate is too high, and the target adjustment coefficient needs to be reduced to reduce the call loss rate; when the first call loss rate is smaller than the minimum call loss rate threshold and the first average idle time is longer than the average idle time threshold, excessive seat resource waste can be caused, and the call quantity is required to be improved by increasing the target adjustment coefficient, so that the seat resource waste is avoided. Therefore, the comparison result of the first call loss rate and the threshold value of the call loss rate, and the comparison result of the first average idle time period and the threshold value of the average idle time period can be used as one of the rate adjustment conditions.
Based on the method for determining the exhalation rate provided by the embodiment, correspondingly, the application further provides a specific implementation mode of the exhalation rate determining device. Please refer to the following examples.
Referring first to fig. 4, an apparatus 400 for determining an outgoing call rate according to an embodiment of the present application includes the following modules:
an obtaining module 401, configured to obtain a historical incoming call amount of a customer service system at a first moment;
an input module 402, configured to input the historical incoming call amount into a differential autoregressive moving average model, and predict a first incoming call amount of a period next to the first time according to the differential autoregressive moving average model;
a determining module 403, configured to determine a first outgoing call rate of the next period according to the first incoming call volume.
The device can predict the first incoming call quantity of the next period of the first moment through the differential autoregressive moving average model and the historical incoming call quantity, after the first incoming call quantity is obtained through prediction, the first outgoing call rate can be further determined in real time according to the first incoming call quantity, the outgoing call rate can be changed along with the change of the incoming call quantity, and when the incoming call quantity is increased, the outgoing call rate is properly reduced, so that the technical problem of higher call loss rate under the condition that the existing incoming call quantity is increased is solved.
As an implementation manner of the present application, in order to ensure accuracy of incoming call amount prediction, the outgoing call rate determining apparatus 400 may further include:
the first acquisition unit is used for acquiring the autoregressive order and the moving average order of the customer service system;
a first determining unit configured to determine an autoregressive model according to the historical call-in amount and the autoregressive order;
a second determining unit configured to determine a moving average model according to the historical call-in amount and the moving average order;
and the merging unit is used for merging the autoregressive model and the moving average model to obtain the differential autoregressive moving average model.
As an implementation manner of the present application, to improve the real-time performance and accuracy of the adjustment of the exhalation rate, the determining module 403 may include:
the second acquisition unit is used for acquiring the number of target agents at the first moment, the number of idle target agents at the first moment and the average service duration of the target agents in the last period of the period in which the first moment is positioned, wherein the target agents comprise mixed sign agents and agents only processing incoming call services, and the mixed sign agents are agents processing both incoming call services and outgoing call services;
The input unit is used for inputting the number of target agents at the first moment, the number of idle target agents at the first moment, the average service duration of the target agents in the last period and the first incoming call quantity into a first model constructed in advance to obtain the quantity of the incoming call at the second moment, wherein the second moment is any moment in the next period;
and a third determining unit, configured to determine the first exhalation rate according to the exhalation amount.
As an implementation manner of the present application, to improve the real-time performance of the adjustment of the exhalation rate, the determining module 403 may include:
a fourth determining unit, configured to determine a target adjustment coefficient according to a first call loss rate of a previous period of the customer service system;
and a fifth determining unit, configured to determine a first exhalation rate according to the first exhalation amount and the target adjustment coefficient.
As an implementation manner of the present application, in order to automatically adjust the target adjustment coefficient, the fourth determining unit may include:
an acquisition subunit, configured to acquire a history adjustment coefficient;
the first adjusting subunit is configured to reduce the historical adjusting coefficient according to the target seat number and a preset ratio to obtain the target adjusting coefficient when the first call loss rate is greater than a maximum call loss rate threshold;
And the second adjusting subunit is used for increasing the history adjusting coefficient according to a preset proportion according to the number of the target agents and the average idle time length of the last period of the target agents under the condition that the first call loss rate is smaller than the minimum call loss rate threshold and the first average idle time length is larger than the average idle time length threshold, so as to obtain the target adjusting coefficient.
As an implementation manner of the present application, the fourth determining unit may include:
the coefficient determining subunit is used for determining a target adjusting coefficient according to the first call loss rate of the previous period of the customer service system under the condition that the customer service system meets the rate adjusting condition;
the rate adjustment conditions include at least one of:
the duration of starting the calling task by the customer service system is greater than or equal to a first duration threshold;
the first moment is not in the adjustment observation period;
the first call loss rate is larger than a maximum call loss rate threshold, or the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold.
The apparatus for determining an exhalation rate according to the embodiment of the present application can implement each step in the foregoing method embodiment, and in order to avoid repetition, details are not repeated here.
Fig. 5 shows a schematic hardware structure of an outgoing call rate determining device according to an embodiment of the present application.
The device may include a processor 501 and a memory 502 storing computer program instructions at the rate of exhalation.
In particular, the processor 501 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 502 may include mass storage for data or instructions. By way of example, and not limitation, memory 502 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 502 may include removable or non-removable (or fixed) media, where appropriate. Memory 502 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 502 is a non-volatile solid state memory.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 501 implements any one of the exhalation rate determination methods of the above embodiments by reading and executing computer program instructions stored in the memory 502.
In one example, the exhalation rate determination device may also include a communication interface 503 and a bus 510. As shown in fig. 5, the processor 501, the memory 502, and the communication interface 503 are connected to each other by a bus 510 and perform communication with each other.
The communication interface 503 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
Bus 510 includes hardware, software, or both, that couple the components of the exhalation rate determination device to one another. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 510 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
The exhalation rate determination apparatus may be based on the above-described embodiments, thereby implementing the exhalation rate determination method and apparatus described in conjunction.
In addition, in combination with the method for determining the calling rate in the above embodiment, the embodiment of the present application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by the processor, implement any one of the methods for determining the calling rate in the foregoing embodiments, and achieve the same technical effects, and in order to avoid repetition, will not be described herein. The computer readable storage medium may include a non-transitory computer readable storage medium, such as Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, and the like, but is not limited thereto.
In addition, the embodiment of the application also provides a computer program product, which comprises computer program instructions, wherein the computer program instructions can realize the steps and corresponding contents of the embodiment of the method when being executed by a processor.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood 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 which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (11)
1. A method of determining an exhalation rate, the method comprising:
acquiring historical incoming call quantity of a customer service system at a first moment;
inputting the historical incoming call quantity into a differential autoregressive moving average model, and predicting a first incoming call quantity of a period next to the first moment through the differential autoregressive moving average model;
and determining a first expiration rate of the next period according to the first inflow amount.
2. The method of claim 1, wherein the inputting the historical incoming call volume into a differential autoregressive moving average model, the method further comprising, prior to predicting a first incoming call volume from the differential autoregressive moving average model:
acquiring an autoregressive order and a moving average order of a customer service system;
determining an autoregressive model according to the historical call volume and the autoregressive order;
determining a moving average model according to the historical call-in quantity and the moving average order;
and combining the autoregressive model and the moving average model to obtain the differential autoregressive moving average model.
3. The exhalation rate determination method according to claim 2, characterized in that the autoregressive model is:
The moving average model is:
the differential autoregressive moving average model is:
wherein mu 1 As a first constant term coefficient, y t1 Predicting incoming call volume for autoregressive model, E t1 Mu, as autoregressive model error value 2 As a second constant term coefficient, y t2 Prediction for moving average modelsIncoming volume, E t2 To move the average model error value, y t For the first incoming call quantity, μ is a constant term coefficient, p is an autoregressive order, q is a moving average order, γ i Is an autocorrelation coefficient, y t-i For incoming call volume of historic t-i time period, e t is current error value, θ i For the correlation coefficient of the moving average model, ε t-i is the error value of the t-i th period.
4. The outgoing call rate determination method according to claim 1, characterized in that the determining the first outgoing call rate of the next period according to the first incoming call volume includes:
acquiring the number of target agents at the first moment, the number of idle target agents at the first moment and the average service duration of the target agents in the last period, wherein the target agents comprise mixed sign agents and agents only processing incoming call services, and the mixed sign agents are agents processing both the incoming call services and the outgoing call services;
inputting the number of target agents at the first moment, the number of idle target agents at the first moment, the average service duration of the target agents in the last period and the first incoming call quantity into a first model constructed in advance to obtain a quantity of the incoming call at a second moment, wherein the second moment is any moment in the next period;
And determining the first expiration rate according to the amount to be expired.
5. The outgoing call rate determination method according to claim 1, characterized in that the determining the first outgoing call rate of the next period according to the first incoming call volume includes:
determining a target adjusting coefficient according to a first call loss rate of a period on the customer service system;
and determining a first calling rate according to the first calling quantity and the target regulating coefficient.
6. The method of claim 5, wherein determining the target adjustment factor based on the first call loss rate for the previous cycle of the customer service system comprises:
acquiring a history adjustment coefficient;
when the first call loss rate is larger than the maximum call loss rate threshold, reducing the historical adjustment coefficient according to the number of the target agents and a preset proportion to obtain the target adjustment coefficient;
and under the condition that the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold, increasing the historical adjustment coefficient according to a preset proportion according to the number of the target agents and the average idle time length of the target agents in the last period to obtain the target adjustment coefficient.
7. The method of claim 5, wherein determining the target adjustment factor based on the first call loss rate for the previous cycle of the customer service system comprises:
under the condition that the customer service system meets the speed regulation condition, determining a target regulation coefficient according to a first call loss rate of a previous period of the customer service system;
the rate adjustment conditions include at least one of:
the duration of starting the calling task by the customer service system is greater than or equal to a first duration threshold;
the first moment is not in the adjustment observation period;
the first call loss rate is larger than a maximum call loss rate threshold, or the first call loss rate is smaller than a minimum call loss rate threshold and the first average idle time length is larger than an average idle time length threshold.
8. An exhalation rate determination apparatus, the apparatus comprising:
the acquisition module is used for acquiring the historical incoming call quantity of the customer service system at the first moment;
the input module is used for inputting the historical incoming call quantity into a differential autoregressive moving average model, and predicting a first incoming call quantity of a period next to the first moment through the differential autoregressive moving average model;
and the determining module is used for determining the first calling rate of the next period according to the first calling quantity.
9. An outgoing call rate determination apparatus, characterized in that the outgoing call rate determination apparatus comprises: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the exhalation rate determination method of any of claims 1-7.
10. A computer storage medium having stored thereon computer program instructions which when executed by a processor implement the method of determining an exhalation rate as claimed in any of claims 1 to 7.
11. A computer program product, characterized in that the computer program product comprises computer program instructions which, when executed by a processor, implement the method of determining the rate of exhalation of any of claims 1-7.
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| CN202211360595.2A CN116915905A (en) | 2022-11-02 | 2022-11-02 | Method, apparatus, device, storage medium and program product for determining calling rate |
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| CN202211360595.2A CN116915905A (en) | 2022-11-02 | 2022-11-02 | Method, apparatus, device, storage medium and program product for determining calling rate |
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