CN110147905B - Information processing method, device, system and storage medium - Google Patents
Information processing method, device, system and storage medium Download PDFInfo
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- CN110147905B CN110147905B CN201910382630.2A CN201910382630A CN110147905B CN 110147905 B CN110147905 B CN 110147905B CN 201910382630 A CN201910382630 A CN 201910382630A CN 110147905 B CN110147905 B CN 110147905B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The present disclosure provides an information processing method, including: acquiring user characteristics of M users waiting for reception; acquiring customer service characteristics of N customer services of non-hospitality users; determining the predicted time length of each user in the N customer service receptions M users according to the user characteristics of the M users and the customer service characteristics of the N customer services to obtain a predicted time length set; and determining at least one served user matched with at least one customer service in the N customer services according to the prediction time length set so as to minimize the sum of the prediction time lengths of the at least one served user matched with the customer service, wherein each customer service corresponds to the matched served user one to one, and M and N are natural numbers. The present disclosure also provides an information processing apparatus, an information processing system, and a computer-readable storage medium.
Description
Technical Field
The present disclosure relates to an information processing method, apparatus, system, and storage medium.
Background
In order to improve customer experience, customer service systems that provide services to customers have been developed. The setting of manual customer service is indispensable in view of the special needs of the customers.
In the existing customer service system, manual customer service and customers are distributed by adopting a sequencing method based on the sequence of access time. Or in consideration of the personal requirements of the customers, a private queue can be established for each customer service in a mode that the customers specify the customer service, so that the customer service only receives the customers in the private queue. Although the sequencing method is simple and easy to implement, the difference between customer service and individual customers is not considered, so that the overall reception efficiency is low. Considering that the private queue needs customer-specified service to be established, the customer often specifies the service rarely, so the method for establishing the private queue is difficult to implement. In summary, the method for distributing customers for manual customer service in the prior art cannot effectively consider individual differences, so that the customer service efficiency is low, and the anxiety degree of customer waiting is high.
Disclosure of Invention
One aspect of the present disclosure provides an information processing method for improving customer service reception efficiency, the method including: acquiring user characteristics of M users waiting for reception; acquiring customer service characteristics of N customer services of non-hospitality users; determining the predicted time length of each user in the N customer service receptions M users according to the user characteristics of the M users and the customer service characteristics of the N customer services to obtain a predicted time length set; and determining at least one served user matched with at least one customer service in the N customer services according to the prediction time length set so as to minimize the sum of the prediction time lengths of the at least one served user matched with the customer service, wherein each customer service corresponds to the matched served user one to one, and M and N are natural numbers.
Optionally, the determining the predicted time length of each of the N customers serving and attending to M users to obtain the predicted time length set includes: taking the user characteristics of M users and the customer service characteristics of N customer services as the input of a machine learning model, and outputting to obtain a prediction duration set; and/or the user characteristics comprise characteristics indicating user request information, and the user request information is used for representing the type of the request information of the user; and/or the customer service characteristics comprise characteristics indicating customer service reception information, and the customer service reception information is used for representing the type of request information processed by the customer service.
Optionally, the information processing method further includes: acquiring reception parameters of at least one user to be received matched with the customer service reception, wherein the reception parameters comprise reception duration and a reception result, and the reception result is used for representing whether request information is successfully processed or not; and optimizing the machine learning model according to the reception parameters.
Optionally, the optimizing the machine learning model according to the hospitality parameters includes: and under the condition that the reception result of the received user matched with the first customer service reception in at least one customer service represents the successful processing request information, taking the reception duration of the received user matched with the first customer service reception as a training target, and taking the customer service characteristics of the first customer service and the user characteristics of the received user matched with the first customer service as samples to train a machine learning model.
Optionally, the determining, according to the predicted duration set, at least one served user matched with at least one of the N customer services includes: determining the size relationship between M and N; and determining at least one to-be-served user matched with at least one customer service in the N customer services according to the size relation between the M and the N and the prediction time length set.
Optionally, the determining, according to the size relationship between M and N and the predicted time length set, at least one served user matched with at least one of the N customer services includes: determining M users as the received users under the condition that M is not larger than N; and determining the users to be served matched with M customer services in the N customer services according to the predicted time length set.
Optionally, the determining, according to the size relationship between M and N and the predicted duration set, at least one served user that matches at least one of the N customer services includes: when M is larger than N, sequencing M users in sequence according to a preset rule, wherein the prediction rule comprises the magnitude sequence of the user waiting time length values and/or the high-low sequence of the user grades; determining the first N users sequenced in sequence as the received users; and determining the received users matched with the N customer services according to the predicted time lengths of the N customer service receiving received users in the predicted time length set.
Another aspect of the present disclosure provides an information processing apparatus including a user characteristic acquisition module, a customer service characteristic acquisition module, a predicted duration determination module, and a matching module. The user characteristic acquisition module is used for acquiring the user characteristics of M users waiting for reception. The customer service characteristic acquisition module is used for acquiring the customer service characteristics of N customer services of the unsuccessfully-served users. The predicted time length determining module is used for determining the predicted time length of each user in the M users for the N customer service according to the user characteristics of the M users and the customer service characteristics of the N customer services, and obtaining a predicted time length set. The matching module is used for determining at least one to-be-served user matched with at least one customer service in the N customer services according to the predicted time length set so as to enable the sum of the predicted time lengths of the to-be-served users matched with the at least one customer service to be minimum. Wherein, each customer service corresponds to the matched received user one by one, and M and N are natural numbers.
Optionally, the predicted duration determining module is specifically configured to: and taking the user characteristics of the M users and the customer service characteristics of the N customer services as the input of the machine learning model, and outputting to obtain a prediction duration set. And/or, the user characteristics include characteristics indicating user request information for characterizing a type of the request information of the user. And/or the customer service characteristics comprise characteristics indicating customer service reception information, and the customer service reception information is used for representing the type of request information processed by the customer service.
Optionally, the information processing apparatus further includes a reception parameter obtaining module and a model optimizing module. The reception parameter acquiring module is used for acquiring reception parameters of at least one user to be received matched with the customer service reception, wherein the reception parameters comprise reception duration and a reception result, and the reception result is used for representing whether the request information is successfully processed. The model optimization module is used for optimizing the machine learning model according to the reception parameters.
Optionally, the model optimization module is specifically configured to, in the case that a reception result of a user to be serviced matched with a first customer service in at least one customer service represents successful processing of request information, train the machine learning model by using a reception duration of the user to be serviced matched with the first customer service as a training target and using a customer service feature of the first customer service and a user feature of the user to be serviced matched with the first customer service as samples.
Optionally, the matching module includes a relationship determination sub-module and a matching sub-module. And the relation determining submodule is used for determining the size relation between M and N. And the matching submodule is used for determining at least one to-be-received user matched with at least one customer service in the N customer services according to the size relation between the M and the N and the prediction time length set.
Optionally, the matching sub-module includes a first serving user determining unit and a first matching user determining unit. The first reception user determining unit is used for determining that the M users are the received users under the condition that M is not larger than N. The first matching user determining unit is used for determining the users to be served matched with M customer services in the N customer services according to the prediction time length set.
Optionally, the user characteristics include a characteristic indicating a user waiting time and/or a characteristic indicating a user level, and the matching sub-module includes a sorting unit, a second user receiving determination unit, and a second matching user determination unit. The sorting unit is used for sorting the M users in sequence according to a preset rule under the condition that M is larger than N, wherein the preset rule comprises the magnitude sequence of the user waiting time length values and/or the height sequence of the user grades. The second reception user determining unit is used for determining the first N users which are sequentially sequenced as the received users. The second matching user determining unit is used for determining the received users matched with the N customer services according to the predicted time lengths of the N customer service receiving received users in the predicted time length set.
Another aspect of the present disclosure provides an information handling system comprising one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above-described information processing method.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, cause the processor to perform the above-described information processing method.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions for implementing the information processing method as described above when executed.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario diagram of an information processing method, apparatus, system, and storage medium according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow chart of an information processing method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates an input-output flow diagram of an information processing method according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram of determining a matching hospitalized user in accordance with an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow diagram of determining a matching hospitalized user according to another embodiment of the present disclosure;
FIG. 6 schematically shows a flow chart of an information processing method according to another embodiment of the present disclosure;
FIG. 7 schematically shows a block diagram of an information processing apparatus according to a disclosed embodiment; and
fig. 8 schematically shows a block diagram of the structure of an information processing system adapted to execute the information processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, 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, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides an information processing method, device and system for improving customer service reception efficiency and a storage medium. The information processing method comprises the following steps: acquiring user characteristics of M users waiting for reception; acquiring customer service characteristics of N customer services of non-hospitality users; determining the predicted time length of each user in the N customer service receptions M users according to the user characteristics of the M users and the customer service characteristics of the N customer services to obtain a predicted time length set; and determining at least one served user matched with at least one customer service in the N customer services according to the prediction time length set so as to minimize the sum of the prediction time lengths of the at least one served user matched with the customer service, wherein each customer service corresponds to the matched served user one to one, and M and N are natural numbers.
According to the information processing method, when the user to be served is allocated to the customer service, the user to be served can be dynamically adjusted in real time according to the user characteristics and the customer service characteristics, so that the sum of the overall prediction duration is minimum, the customer service serving efficiency can be effectively improved, the user satisfaction is improved to a certain extent, and the problem of waiting time of different users is effectively balanced.
Fig. 1 schematically illustrates an application scenario diagram of an information processing method, apparatus, system, and storage medium according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, an application scenario 100 of an embodiment of the present disclosure includes at least one customer service, at least one user, and a server 130.
The at least one customer service may include, for example, customer services 111, 112, and 113, and the at least one customer service may specifically be, for example, a customer service currently in an idle state or a busy state, so as to serve the user and provide services for the user.
The at least one user may include, for example, users 121, 122, 123, and the at least one user may initiate, for example, a customer service request through the first terminal in order to obtain help of customer service. The terminal may include, but is not limited to, a cell phone, a smart watch, a tablet computer, a laptop portable computer, a desktop computer, and the like, among others. The manner in which the service request is initiated may include, for example, a manner in which a user dials a service hotline using a terminal. Alternatively, the manner of initiating the customer service request may be a manner of initiating an online request using various applications of the terminal. Accordingly, the terminal should be connected to a communication network, such as a 2G, 3G, 4G or even 5G communication network or a wireless local area network, and various communication client applications, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, etc. (for example only) may also be installed on the terminal.
According to the embodiment of the present disclosure, the customer service request initiated by the at least one user may be specifically, for example, sent to the server 130 through a network, and forwarded by the server 130 to the at least one customer service 111, 112, 113. The network may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others. The server 130 may be, for example, a server that provides various services, such as receiving a customer service request from a user and distributing the customer service request from the user to a customer service.
Accordingly, the at least one customer service may respond to the customer service request of the user, for example, through the second terminal, to provide corresponding assistance to the user. The second terminal may be, for example, a terminal of the same type as or similar to the first terminal, and may also communicate with the server 130 through the network, which is not described herein again. According to the embodiment of the present disclosure, the at least one customer service may further send a real-time status to the server through the second terminal, for example, after the user is received, send feedback information to the server 130 to indicate that the user currently belongs to an idle status, and when the user is received just beginning, send the receiving information to the server 130 to indicate that the user currently belongs to a busy status, and the like.
According to the embodiment of the present disclosure, the server 130 may further obtain, for example, a user characteristic of a user initiating a customer service request in a waiting state through a first terminal, obtain a customer service characteristic of a customer service through a second terminal, and obtain the user characteristic and the customer service characteristic from a database or locally according to the obtained user characteristic, the obtained customer service characteristic, and a customer service state of the at least one customer service, so as to allocate a user to the at least one customer service according to the user characteristic and the customer service characteristic, so that the at least one customer service responds to a customer service request of an allocated user.
It should be noted that the information processing method provided by the embodiment of the present disclosure may be generally executed by the server 130. Accordingly, the information processing apparatus provided by the embodiment of the present disclosure may be generally disposed in the server 130. The information processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 130 and is capable of communicating with the first terminal and the second terminal, and/or the server 130. Accordingly, the information processing apparatus provided in the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 130 and capable of communicating with the first terminal and the second terminal, and/or the server 130.
It should be understood that the type and number of servers, and the number of users and customer services in FIG. 1 are merely illustrative. There may be any type and number of servers, and any number of users and customer services, as desired for an implementation.
Fig. 2 schematically shows a flow chart of an information processing method according to an embodiment of the present disclosure. Fig. 3 schematically shows an input-output flowchart of an information processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the information processing method of the embodiment of the present disclosure includes operations S210 to S240.
In operation S210, user characteristics of M users waiting for reception are acquired.
The M users waiting for reception may refer to users 121, 122, and 123 in fig. 1, for example, where M is a natural number. The M users are specifically users who have sent the customer service request but have not responded to the customer service request. In an application scenario, the M users may be, for example, users who have dialed a service hotline through a terminal, but have not yet been answered. The user profile may specifically be obtained by referring to the server 130 in fig. 1, and specifically may be retrieved from the server local or database according to the ID tag of the first terminal sending the customer service request. The server should store the corresponding relationship between the ID tag and the user feature corresponding to each user one-to-one in advance locally or in the database. The user characteristics in the stored correspondence relationship may be obtained by, for example, accumulating and extracting the historical customer service request information of the user and the response information to the historical customer service request. According to an embodiment of the present disclosure, the user characteristic may also be obtained by the server 130 in real time.
According to an embodiment of the present disclosure, the user characteristics may include, for example, a characteristic indicating that there is user request information for characterizing a type of the user's request information. Alternatively, the user characteristics may also include multidimensional characteristics indicating information such as the user's age (a), gender(s), academic calendar (e), level (l), and average call duration (t), so that the user characteristics are more indicative of the user's personal needs. According to an embodiment of the present disclosure, the user characteristics of the M users obtained in operation S210 may be represented by a matrix X, for examplecRepresented by (A, S, T, E, L … …), wherein A, S, T, E, L represents a vector of values indicating the age a, gender s, average call duration t, academic calendar e, and rank l of the M users, respectively. Accordingly, as shown in FIG. 3, the user characteristics of the ith user may be expressed as
In operation S220, customer service characteristics of N customer services of the unsuccessfully served user are obtained.
The N customer services not receiving the user may be, for example, the customer services in an idle state in the customer services 111, 112, and 113 in fig. 1, where N is a natural number. In an application scenario, the N customer services may be customer services that have not received a new customer service hot line after receiving the customer service hot line dialed by the user. The service feature may specifically be obtained by referring to the server 130 in fig. 1, and specifically may be retrieved from a local server or a database according to the ID tag of the second terminal feeding back the service status. The server should store in advance the corresponding relationship between the ID tag and the service characteristics, which are in one-to-one correspondence with each service.
According to embodiments of the present disclosure, the customer service characteristics may include, for example, characteristics indicative of customer service hospitality information for characterizing the type of request information (customer service request) that the customer service is capable of handling. Alternatively, the customer service features may also include fingersAnd displaying multidimensional characteristics of information such as customer service age (a), working age (w), gender(s), academic calendar (e), average reception duration (t), grade (l) and the like, so that the customer service characteristics can better indicate the service capacity of the customer service. According to an embodiment of the present disclosure, the customer service characteristics of the N customer services obtained in the operation S220 may be represented by a matrix X, for examplesRepresented by (A, W, S, T, E, L … …), wherein A, W, S, T, E, L represents a vector composed of values indicating the age a, the working age w, the sex s, the average reception time t, the academic calendar e, and the rating l of the N customer services, respectively. Accordingly, as shown in FIG. 3, the customer service characteristic of the jth customer service can be expressed as
In operation S230, the predicted duration of each user in the N customers serving M users is determined according to the user characteristics of the M users and the customer service characteristics of the N customers, so as to obtain a predicted duration set.
According to an embodiment of the present disclosure, the operation may specifically be, for example: and taking the user characteristics of the M users and the customer service characteristics of the N customer services as the input of the machine learning model, and outputting to obtain a prediction duration set. Specifically, the matrix X of the user characteristics iscAnd matrix X of customer service characteristicssInput to machine learning models such as trees, neural networks, Bayesian networks, etc
In the method, the output obtains a prediction time length set Tp. Wherein the predicted duration set TpThe method specifically includes N × M predicted time length values, that is, the predicted time length values of each customer service in the N customer services for each user in the M users.
In operation S240, at least one hospitalized user matched with at least one customer service of the N customer services is determined according to the predicted duration set, so that a sum of predicted durations of the at least one customer service matched hospitalized user is minimized.
According to an embodiment of the present disclosure, each of the at least one customer service finally determined in operation S240 is in one-to-one correspondence with the matched hospitalized user. Specifically, the operation S240 may include the following steps: firstly, a plurality of user customer service matching groups which possibly exist are determined, wherein each user customer service matching group is matched with customer service one by one. And then determining the reception duration of each customer service reception paired user in each user customer service pairing group according to the prediction duration set. And then calculating the sum of the reception duration of all the users in the customer service reception pairing group of each user. And finally, determining one group with the minimum sum of the reception duration as an optimal user customer service matching group from a plurality of user customer service matching groups, wherein the user and the customer service which are matched one by one in the optimal user customer service matching group are finally determined optimal user and customer service combinations, and the user is at least one user to be received matched with at least one customer service.
According to an embodiment of the present disclosure, the operation of determining the matched user to be served in operation S240 may further include the operations described with reference to fig. 4 to 5, for example, and will not be described in detail here.
In summary, in the information processing method according to the embodiment of the present disclosure, since the predicted duration of each user for customer service reception is determined based on the user characteristics and the customer service characteristics, and the needs of special users and the field of adequacy of customer service are considered, the predicted duration of customer service reception for the customer service reception user that can solve the user request is shorter, and the predicted duration of customer service reception for the customer service reception user that cannot solve the user request is longer, that is, the determined predicted duration is more in line with the actual needs, and the accuracy is higher. Moreover, when finally determining the matched customer service time of the user, the matching combination with the minimum total prediction time length is determined, so that the customer service receiving efficiency can be effectively improved, the waiting time length of the user can be reduced to a certain extent, and the user experience is improved.
Fig. 4 schematically illustrates a flow chart of a determination of a matching hospitalized user according to an embodiment of the present disclosure.
According to an embodiment of the present disclosure, as shown in fig. 4, the operation S240 in fig. 2 may specifically include, for example, operations S441 to S442.
In operation S441, a size relationship of M to the N is determined. In operation S442, at least one hospitalized user matched with at least one of the N customer services is determined according to the size relationship between M and N and the set of predicted durations.
Operation S441 specifically compares the magnitude relationship between M and N. When M is not less than N, in operation S442, N users of the M users may be determined as the users to be served, then all possible combinations of the N customer services and the N users are determined, then the sum of the predicted durations of each combination in all the combinations is determined, and finally the combination with the smallest sum of the predicted durations is determined from all the combinations, so that the N users to be served that are matched with the N customer services one by one may be determined. If M is smaller than N, in operation S442, M customer services may be determined from the N customer services, and used as customer services of the user to be serviced, and the M users are used as the user to be serviced; and then determining all possible combinations of the M customer services and the M users, determining the sum of the predicted duration of each combination in all the possible combinations, and finally determining the combination with the minimum sum of the predicted durations from all the combinations, thereby determining the M customer services matched with the M users to be served one by one. It is to be understood that the above method for determining the matched user to be served is only an example to facilitate understanding of the present disclosure, and the present disclosure is not limited thereto, for example, the method may also adopt the method described in fig. 5, and will not be described in detail herein.
Fig. 5 schematically illustrates a flow diagram of a determination of a matching hospitalized user according to another embodiment of the present disclosure.
According to the embodiment of the present disclosure, as shown in fig. 5, the method of the embodiment of the present disclosure may start with the determination operation S541, and then perform the subsequent operation according to the determination result. That is, operation S441 in fig. 4 may specifically be determining operation S541: and judging whether M is larger than N. If M is greater than N, operations S542 to S544 are performed. If M is not greater than N, operations S545-S546 are performed.
When M is larger than N, operation S542 is executed, and M users are sequentially ordered according to a preset rule; operation S543, determining the top N sequentially ranked users as the received users; and operation S544, determining the served users matched with the N customer services according to the predicted durations of the N customer service served users in the predicted duration set. Wherein, operation S544 may specifically include: and determining the served user matched with each customer service in the N customer services according to the predicted time length of each served user in the N served users determined in the operation S543 for receiving each customer service in the N customer services. According to an embodiment of the present disclosure, the operation S544 may specifically further include: first all possible combinations of N customer services and N users to be served are determined, resulting in N! A combination of two; then, the sum of the predicted duration of the received user corresponding to the N customer service receptions in each combination is calculated to obtain N! Sum of predicted durations; then from the N! And determining the minimum value in the sum of the predicted time lengths, wherein the sum of the predicted time lengths takes the served users corresponding to the N customer services in the combination of the minimum values as the finally determined matched served users.
According to the embodiment of the present disclosure, the user characteristics may further include, for example, a characteristic indicating a waiting time of the user and/or a characteristic indicating a user level l. The preset rules may include, for example, the magnitude order of the values of the user waiting time periods and/or the high-low order of the user grades. Specifically, the preset rule may be a magnitude order of the values of the user waiting durations, and operation S542 may specifically be to sort the M users in sequence according to the order of the values of the waiting durations from large to small. If the preset rule may be the order of the user levels, then operation S542 may specifically be to sort the M users in sequence according to the order from the level to the level. It is understood that the operation S542 may also comprehensively consider the magnitude order of the values of the user waiting time periods and the high-low order of the user ranks. Specifically, for example, the M users may be sequentially ordered according to the sequence of the waiting duration from large to small; and then adjusting the sequence of the users with high rank but later sequence, and adjusting the sequence of the user to the position with the former sequence or adjusting the sequence of the user to a fixed position. Or the M users can be sequentially sorted according to the sequence from the high level to the low level; and then adjusting the sequence of the user with long waiting time but later sequence, and adjusting the sequence of the user to the position of the user at the front of the sequence, or adjusting the sequence of the user to a fixed position. Or, a normalized coefficient may be allocated to the waiting duration and the level of the user, a ranking parameter related to the waiting duration and the level of each of the M users is obtained through calculation, and then the M users are sequentially ranked according to a sequence of values of the ranking parameter from large to small, and the like. It is to be understood that the specific implementation manner of operation S542 described above is merely an example to facilitate understanding of the present disclosure, and the present disclosure is not limited thereto.
If M is not greater than N, performing operation S545, and determining M users as the received users; and operation S546, determining the served users matched with M customer services of the N customer services according to the predicted duration set. Wherein, the operation S546 specifically includes: and determining the served users matched with the M customer services in the N customer services according to the predicted time length of each customer service in the N customer services in the predicted time length set to serve each user in the M served users. According to an embodiment of the present disclosure, the operation S546 may specifically include: first all possible combinations of N customer services and M hospitalized users are determined, resulting in N! L (N-M)! A combination of two; then, the sum of the predicted duration of the users to be served corresponding to the M customer service waiters in each combination is calculated to obtain N! L (N-M)! Sum of predicted durations; then from the N! L (N-M)! If the minimum value is determined from the sum of the predicted durations, the predicted duration sum takes the customer services corresponding to the M users to be served in the combination of the minimum values, which are the M customer services described in operation S546, so as to obtain the users to be served matched with the M customer services.
According to an embodiment of the present disclosure, in the above operations S544 and S546, specifically, for example, an algorithm in the prior art, such as a bipartite graph maximum matching algorithm, may also be used to determine the correspondence between the customer service and the received user, so as to minimize the sum of the determined predicted durations of the reception of all the received users.
In summary, according to the method for determining the matched user to be served in the embodiment of the present disclosure, the total duration of the final customer service for the user to be served is ensured to be minimum, so that the customer service efficiency can be effectively improved, the waiting duration of the user is reduced, and the user experience is improved.
Fig. 6 schematically shows a flow chart of an information processing method according to another embodiment of the present disclosure.
As shown in fig. 6, the information processing method according to the embodiment of the present disclosure may further include operations S650 to S660, in addition to operations S210 to S240 described in fig. 2.
According to an embodiment of the present disclosure, the predicted duration set in operation S230 may be obtained, for example, by referring to operation S630 in fig. 6, that is, the user characteristics of M users and the customer service characteristics of N customer services are used as input of the machine learning model, and the predicted duration set is obtained through output.
In operation S650, a hospitality parameter of at least one hospitalized user matched with the customer service is obtained. In operation S660, the machine learning model is optimized according to the reception parameter.
The reception parameter obtained in operation S650 may be obtained by real-time monitoring by the server 130 in fig. 1, or may be sent to the server 130 through the second terminal after at least one customer service completes reception. The acquired reception parameters may include a reception duration and a reception result. Wherein, the reception duration is the duration used by the received user matched with the customer service reception. The reception result is used to represent whether the request information is successfully processed, and in an embodiment, the reception result is specifically used to represent whether the customer service has solved the question asked by the user.
The operation S660 is specifically: the user characteristics of the user to be serviced and the matched customer service characteristics determined in operation S240 are used as training samples, and the corresponding service duration obtained in operation S650 is used as a training target to train and optimize the machine learning model, so that the accuracy of the predicted duration can be improved in the subsequent use process of the machine learning model.
According to an embodiment of the present disclosure, in order to avoid an influence of invalid reception on the accuracy of the machine learning model, the operation S660 may specifically include: firstly, whether the reception result representation in the reception parameters obtained in operation S650 represents successful processing of the request information or unsuccessful processing of the request information is determined; and then, only when the reception result represents that the request information is successfully processed, the user characteristics of the user to be received corresponding to the reception result and the matched customer service characteristics are taken as training samples to carry out optimization training on the machine learning model, and when the reception result represents that the request information is not successfully processed, the machine learning model is not subjected to optimization training. That is, operation S660 may specifically be: and under the condition that the reception result of the received user matched with the first customer service reception in at least one customer service represents the successful processing request information, taking the reception duration of the received user matched with the first customer service reception as a training target, and taking the customer service characteristics of the first customer service and the user characteristics of the received user matched with the first customer service as samples to train a machine learning model. Therefore, compared with the technical scheme of directly optimizing the machine learning model according to all reception parameters, the technical scheme of the embodiment can further improve the precision of the machine learning model obtained through optimization, so that the accuracy of the prediction duration obtained by the machine learning model in the subsequent use process is improved. Therefore, the accuracy of customer service distribution can be further improved to a certain extent, and the customer service processing efficiency and the user experience are improved.
Fig. 7 schematically shows a block diagram of the structure of an information processing apparatus according to the disclosed embodiment.
As shown in fig. 7, the information processing apparatus 700 according to the embodiment of the present disclosure includes a user characteristic obtaining module 710, a customer service characteristic obtaining module 720, a predicted duration determining module 730, and a matching module 740.
The user characteristic obtaining module 710 is configured to obtain user characteristics of M users waiting to be served. The user characteristics may include, for example, user request information that characterizes the type of request information for the user. Where M is a natural number. According to an embodiment of the present disclosure, the user characteristic obtaining module 710 may be configured to perform operation S210 described with reference to fig. 2, for example, and is not described herein again.
The customer service feature obtaining module 720 is configured to obtain customer service features of N customer services of the unsuccessfully served users. The customer service characteristics may include, for example, customer service hospitality information characterizing the type of requested information handled by the customer service. Wherein N is a natural number. According to an embodiment of the disclosure, the customer service characteristic obtaining module 720 may be configured to perform operation S220 described with reference to fig. 2, for example, and is not described herein again.
The predicted duration determining module 730 is configured to determine the predicted duration of each user of the M users for the N customer services to obtain a predicted duration set according to the user characteristics of the M users and the customer service characteristics of the N customer services. According to an embodiment of the present disclosure, the predicted duration determining module 730 may be configured to perform operation S230 described with reference to fig. 2, for example, and is not described herein again.
According to an embodiment of the present disclosure, the predicted duration determining module 730 may be specifically configured to output, as inputs of the machine learning model, the user characteristics of the M users and the customer service characteristics of the N customer services, so as to obtain a predicted duration set. That is, the predicted duration determining module 730 may be further configured to execute operation S630 described with reference to fig. 6, which is not described herein again.
The matching module 740 is configured to determine at least one served user matched with at least one of the N customer services according to the predicted duration set, so as to minimize a sum of the predicted durations of the at least one served user matched with the customer service. According to an embodiment of the present disclosure, each customer service here is in one-to-one correspondence with a matching hospitalized user. The matching module 740 may be configured to perform the operation S240 described with reference to fig. 2, for example, and is not described herein again.
According to an embodiment of the present disclosure, as illustrated in fig. 7, the matching module 740 may specifically include, for example, a relationship determining sub-module 741 and a matching sub-module 742. The relationship determining submodule 741 is configured to determine a size relationship between M and N. The matching sub-module 742 is configured to determine at least one served user that matches at least one of the N customer services according to the size relationship between M and N and the predicted duration set. According to an embodiment of the present disclosure, the relationship determining sub-module 741 and the matching sub-module 742 may be, for example, respectively configured to perform operations S441 to S442 described with reference to fig. 4, and the relationship determining sub-module 741 may be specifically, for example, configured to perform the determining operation S541 described with reference to fig. 5, which is not described herein again.
According to an embodiment of the present disclosure, as shown in fig. 7, the matching sub-module 742 may include, for example, a first pickup user determining unit 7421 and a first matching user determining unit 7422. The first hospitality user determining unit 7421 is configured to determine M users as the hospitalized users if M is not greater than N. The first matching user determination unit 7422 is configured to determine the served users matching with M of the N customer services according to the predicted duration set. According to the embodiment of the present disclosure, the first hospitality user determination unit 7421 and the first matching user determination unit 7422 may be, for example, respectively configured to perform operations S545 to S546 described with reference to fig. 5, which are not described herein again.
According to the embodiment of the disclosure, the user characteristics comprise a characteristic indicating the waiting time of the user and/or a characteristic indicating the user grade. As shown in fig. 7, the matching sub-module 742 may include, for example, a sorting unit 7423, a second user waiting determination unit 7424 and a second user matching determination unit 7425. The sorting unit 7423 is configured to, when M is greater than N, sequentially sort the M users according to a preset rule, where the preset rule includes a magnitude order of values of user waiting durations and/or a high-low order of user levels. The second hospitality user determination unit 7424 is used to determine the first N users in sequence as the hospitalized users. The second matching user determining unit 7425 is configured to determine the received users matched with N customer services according to the predicted duration of the N customer service received users in the predicted duration set. According to the embodiment of the present disclosure, the sorting unit 7423, the second reception user determining unit 7424 and the second matching user determining unit 7425 may be, for example, respectively configured to perform operations S542 to S544 described with reference to fig. 5, and are not described again here.
According to an embodiment of the present disclosure, as shown in fig. 7, the information processing apparatus 700 may further include a reception parameter obtaining module 750 and a model optimizing module 760. The reception parameter acquiring module 750 is configured to acquire a reception parameter of at least one received user matched with the customer service reception. The model optimization module 760 is configured to optimize the machine learning model according to the hospitality parameters. The reception parameters comprise reception duration and a reception result, and the reception result is used for representing whether the request information is successfully processed. According to an embodiment of the present disclosure, the reception parameter obtaining module 750 and the model optimizing module 760 may be, for example, respectively configured to perform operations S650 to S660 described with reference to fig. 6, which are not described herein again.
According to an embodiment of the present disclosure, the model optimization module 760 may be specifically configured to, for example, train a machine learning model by taking a service duration of a served user matched with a first customer service as a training target and taking a customer service feature of the first customer service and a user feature of the served user matched with the first customer service as samples when a service result of the served user matched with the first customer service in at least one customer service represents successful processing of request information.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the user characteristic obtaining module 710, the customer service characteristic obtaining module 720, the predicted duration determining module 730, the matching module 740, the reception parameter obtaining module 750, the model optimizing module 760, the relationship determining sub-module 741, the matching sub-module 742, the first reception user determining unit 7421, the first matching user determining unit 7422, the sorting unit 7423, the second reception user determining unit 7424 and the second matching user determining unit 7425 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the user characteristic obtaining module 710, the customer service characteristic obtaining module 720, the predicted duration determining module 730, the matching module 740, the hospitality parameter obtaining module 750, the model optimizing module 760, the relationship determining sub-module 741, the matching sub-module 742, the first hospitality user determining unit 7421, the first matching user determining unit 7422, the sorting unit 7423, the second hospitality user determining unit 7424 and the second matching user determining unit 7425 may be at least partially implemented as a hardware circuit, such as Field Programmable Gate Arrays (FPGAs), Programmable Logic Arrays (PLAs), systems on a chip, systems on a substrate, systems on a package, Application Specific Integrated Circuits (ASICs), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging circuits, or in any one of three implementations, software, hardware and firmware, or in any suitable combination of any of them. Alternatively, at least one of the user characteristic obtaining module 710, the customer service characteristic obtaining module 720, the predicted duration determining module 730, the matching module 740, the reception parameter obtaining module 750, the model optimizing module 760, the relationship determining sub-module 741, the matching sub-module 742, the first reception user determining unit 7421, the first matching user determining unit 7422, the sorting unit 7423, the second reception user determining unit 7424 and the second matching user determining unit 7425 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.
Fig. 8 schematically shows a block diagram of the structure of an information processing system adapted to execute the information processing method according to an embodiment of the present disclosure.
As shown in fig. 8, information handling system 800 includes a processor 810 and a computer readable storage medium 820. The information processing system 800 may perform a method according to an embodiment of the present disclosure.
In particular, processor 810 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 810 may also include on-board memory for caching purposes. Processor 810 may be a single processing unit or a plurality of processing units for performing different actions of a method flow according to embodiments of the disclosure.
Computer-readable storage medium 820, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 820 may include a computer program 821, which computer program 821 may include code/computer-executable instructions that, when executed by the processor 810, cause the processor 810 to perform a method according to an embodiment of the present disclosure, or any variation thereof.
The computer program 821 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 821 may include one or more program modules, including for example 821A, modules 821B, … …. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, and when the program modules are executed by the processor 810, the processor 810 may execute the method according to the embodiment of the present disclosure or any variation thereof.
According to the embodiment of the present invention, at least one of the user characteristic obtaining module 710, the customer service characteristic obtaining module 720, the predicted duration determining module 730, the matching module 740, the reception parameter obtaining module 750, the model optimizing module 760, the relationship determining sub-module 741, the matching sub-module 742, the first reception user determining unit 7421, the first matching user determining unit 7422, the sorting unit 7423, the second reception user determining unit 7424 and the second matching user determining unit 7425 may be implemented as a computer program module described with reference to fig. 8, which when executed by the processor 810 may implement the corresponding operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (9)
1. An information processing method, the method comprising:
acquiring user characteristics of M users waiting for reception;
acquiring customer service characteristics of N customer services of non-hospitality users, wherein the customer service characteristics comprise characteristics indicating customer service hospitality information, and the customer service hospitality information is used for representing the type of request information processed by the customer service;
determining the predicted time length of each user in the M users for the N customer service according to the user characteristics of the M users and the customer service characteristics of the N customer services to obtain a predicted time length set; and
determining a size relationship of the M to the N,
determining all combinations of N customer services matched with N users in the M users under the condition that M is not less than N, and determining the combination with the minimum sum of the predicted durations in all the combinations according to the predicted duration set so as to determine N users in the M users matched with the N customer services,
determining all combinations of M customer services in the N customer services matched with the M users under the condition that M is smaller than N, and determining the combination with the minimum sum of the predicted durations in all the combinations according to the predicted duration set so as to determine the M users matched with the M customer services in the N customer services,
wherein, each customer service corresponds to the matched received user one by one, and M and N are natural numbers.
2. The method of claim 1, wherein:
the determining the predicted time length for the N customer services to receive each of the M users to obtain a predicted time length set includes: taking the user characteristics of the M users and the customer service characteristics of the N customer services as the input of a machine learning model, and outputting to obtain the predicted duration set;
the user characteristics include characteristics indicating user request information for characterizing a type of request information of the user.
3. The method of claim 2, further comprising:
acquiring the reception parameters of the at least one user to be received matched with the at least one customer service reception, wherein the reception parameters comprise a reception duration and a reception result, and the reception result is used for representing whether the request information is successfully processed or not; and
and optimizing the machine learning model according to the reception parameters.
4. The method of claim 3, wherein optimizing the machine learning model according to the hospitality parameters comprises:
and under the condition that the reception result representation of the received user matched with the first customer service reception in the at least one customer service successfully processes the request information, taking the reception duration of the received user matched with the first customer service reception as a training target, and taking the customer service characteristics of the first customer service and the user characteristics of the received user matched with the first customer service as samples to train the machine learning model.
5. The method of claim 1, wherein said determining at least one hospitalized user matching at least one of said N customer services based on said magnitude relationship between M and N and said set of predicted durations comprises:
determining that the M users are the users to be served if the M is not greater than the N; and
and determining the users to be served matched with M customer services in the N customer services according to the predicted time length set.
6. The method of claim 1, wherein the user characteristics include a characteristic indicating a user wait time duration and/or a characteristic indicating a user rating, and wherein determining at least one served user that matches at least one of the N customer services based on the magnitude relationship between M and N and the set of predicted time durations comprises:
when M is larger than N, sequencing the M users in sequence according to a preset rule, wherein the preset rule comprises the magnitude sequence of the user waiting time length values and/or the high-low sequence of the user grades;
determining the first N users sequenced in sequence as the received users; and
and determining the received users matched with the N customer services according to the predicted time length of the N customer service receiving users in the predicted time length set.
7. An information processing apparatus comprising:
the user characteristic acquisition module is used for acquiring the user characteristics of M users waiting to be served;
the system comprises a customer service characteristic acquisition module, a customer service characteristic acquisition module and a customer service characteristic acquisition module, wherein the customer service characteristic acquisition module is used for acquiring customer service characteristics of N customer services of non-hospitalized users, the customer service characteristics comprise characteristics indicating customer service hospitalization information, and the customer service hospitalization information is used for representing the type of request information processed by the customer service;
the predicted time length determining module is used for determining the predicted time length of each user in the M users for the N customer service to receive according to the user characteristics of the M users and the customer service characteristics of the N customer services to obtain a predicted time length set; and
a matching module, configured to determine a size relationship between M and N, determine all combinations of N customer services matched with N users of the M users when M is not less than N, determine a combination with a smallest sum of predicted durations in all combinations according to the predicted duration set to determine N users of the M users matched with the N customer services, determine all combinations of M customer services of the N customer services matched with the M users when M is less than N, determine a combination with a smallest sum of predicted durations in all combinations according to the predicted duration set to determine the M users matched with M customer services of the N customer services,
wherein, each customer service corresponds to the matched received user one by one, and M and N are natural numbers.
8. An information processing system comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-6.
9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 6.
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