CN114757430A - Service resource allocation method and system - Google Patents

Service resource allocation method and system Download PDF

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CN114757430A
CN114757430A CN202210452409.1A CN202210452409A CN114757430A CN 114757430 A CN114757430 A CN 114757430A CN 202210452409 A CN202210452409 A CN 202210452409A CN 114757430 A CN114757430 A CN 114757430A
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章垚鹏
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Hangzhou Fanshengyouhang Technology Co ltd
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Abstract

On one hand, the method and the system can intelligently allocate advisory service resources to the user in real time, so that the requirements of the user are matched with the characteristics of the advisory service resources, the allocation requirements of the existing insurance scene are fully met, and the timeliness is ensured. On the other hand, by means of automatic matching of the preset distribution rules, business personnel only need to pre-configure some preset distribution rules to input the distribution models to train the distribution models, manual participation is not needed in the actual service resource distribution process, errors caused by human factors can be reduced, and accuracy of service resource distribution is improved.

Description

Service resource allocation method and system
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and a system for allocating service resources.
Background
With the development of the insurance industry, the insurance awareness of people is gradually improved, the purchase of insurance products is more and more close to commercialization, and due to the specialty, complexity and particularity of the insurance products, people want to know more contents about deeper insurance, such as risk assessment, insurance scheme planning, insurance conscientiousness report and the like. Because the user traffic has various sources and different levels of requirements, and the advisor service resources do not have appropriate breach to provide insurance services for the user, the service resources are not distributed reasonably, and the two cannot establish close connection.
In the traditional service resource allocation method, the advisor service resources are allocated only by the characteristics of the user flow through manual intervention, a server side needs to provide data display capable of screening the characteristics of the user flow, and service personnel needs to manually establish the relationship between the user flow and the service resources to finally realize the allocation of the server resources.
However, the method for manually allocating the service resources has the problems of insufficient timeliness and low accuracy. Due to the fact that the behavior time of the user flow on each platform is different, after the user behavior occurs, feature screening and allocation operations performed after the flow reminding is manually acquired are different from the actual user behavior time, and timeliness is insufficient. Moreover, different distributors have different familiarity with different distribution scenarios, which may result in reduced distribution accuracy.
Disclosure of Invention
Therefore, it is necessary to provide a service resource allocation method aiming at the problems of insufficient timeliness and low accuracy of the conventional service resource allocation method.
The application provides a method for allocating service resources, which comprises the following steps:
acquiring a unique identification code of a user terminal, user behavior data and user personalized data;
judging whether the user terminal is a new user terminal or not according to the unique identification code of the user terminal;
if the user terminal is a new user terminal, analyzing the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal according to the user behavior data and the user personalized data of the user terminal;
inputting the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal into the distribution model;
operating the distribution model to obtain a preset distribution rule matched with the user terminal;
screening out an optimal service consultant from the service resource pool according to a preset allocation rule matched with the user terminal;
and establishing communication connection between the user terminal and the service terminal corresponding to the optimal service consultant.
Further, the user behavior data includes one or more of information gathering data, public platform advisory data, and telemarketing data.
Further, the determining whether the ue is a new ue according to the unique ue identity includes:
searching a service consultant unique identification code corresponding to the user terminal unique identification code in the database server by taking the user terminal unique identification code as an index, and judging whether the service consultant unique identification code corresponding to the user terminal unique identification code exists in the database server or not;
and if the unique service advisor identification code corresponding to the unique user terminal identification code does not exist in the database server, determining that the user terminal is a new user terminal.
Further, the operating the distribution model to obtain a preset distribution rule matched with the user terminal includes:
operating the distribution model, and reading the distribution type parameters of the distribution actuators in the distribution model;
acquiring N preset distribution rules corresponding to the distribution type parameters from a database server;
clearing the current data recorded by the counter, and selecting a preset distribution rule from the N preset distribution rules;
judging whether the service scene characteristic attribute and the self demand characteristic attribute of the user terminal hit the preset distribution rule or not;
and if the service scene characteristic attribute and the self demand characteristic attribute of the user terminal hit the preset distribution rule, taking the preset distribution rule as the preset distribution rule matched with the user terminal.
Further, the operating the distribution model to obtain a preset distribution rule matched with the user terminal further includes:
if the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal do not hit the preset allocation rule,
adding 1 to the current data recorded by the counter on the basis of the original data;
judging whether the current data recorded by the counter is less than N;
if the current data recorded by the counter is less than N, returning to the step of selecting one preset distribution rule from the N preset distribution rules;
and if the current data recorded by the counter is greater than or equal to N, replacing the distribution type parameter of the distribution actuator, returning to the operation distribution model, and reading the distribution type parameter of the distribution actuator in the distribution model.
Further, the screening out an optimal service advisor from the service resource pool according to a preset allocation rule matched with the user terminal includes:
taking a preset distribution rule matched with the user terminal as a target distribution rule;
acquiring a service consultant team matched with the target distribution rule from the service resource pool; each service advisor team comprising a plurality of service advisor groups;
calling a preset service advisor group filtering rule, polling the service state of each service advisor group in the service advisor team matched with the target distribution rule in an intergroup polling mode, matching the service state of each service advisor group with the preset service advisor group filtering rule, and taking the service advisor group which hits the preset service advisor group filtering rule and has the optimal service state as an optimal service advisor group;
calling preset service advisor filter rules, polling the service state of each service advisor in the optimal service advisor group in an in-group polling manner, matching the service state of each service advisor group with the preset service advisor group filter rules, and taking the service advisor which hits the preset service advisor filter rules and has the optimal service state as the optimal service advisor.
Further, the invoking of the preset service advisor group filtering rule polls the service state of each service advisor group in the service advisor team matched with the target allocation rule in an intra-group polling manner, matches the service state of each service advisor group with the preset service advisor group filtering rule, and takes the service advisor group having the best service state and hitting the preset service advisor group filtering rule as the optimal service advisor group, comprising:
selecting a service advisor group from the service advisor team matching the target allocation rule;
acquiring the type, the volume, the time interval and the state of the service consultant from the service resource pool by taking the unique identifier of the service consultant group as an index;
calling a preset service advisor group filter rule, and matching each of the type of the received service, the volume of the received service, the time interval of the received service and the state of the received service with the preset service advisor group filter rule;
judging whether each of the type of the received call, the volume of the received call, the time interval of the received call and the state of the received call hits a preset service advisor group filtering rule;
if each of the type of receiving, the amount of receiving, the time interval of receiving and the state of receiving hits the preset service advisor group filtering rules, then the service advisor group is used as a hit service advisor group;
returning to the step of obtaining a service advisor group from the service resource pool by taking the service advisor group unique identifier as an index, and obtaining the type of receiving, the receiving capacity, the receiving time interval and the receiving state of the service advisor group until all service advisor groups in the service advisor team matched with the target distribution rule are selected once to obtain at least one hit service advisor group;
determining whether the total number of hit service advisor groups is equal to 1;
if the total number of hit service advisor groups is equal to 1, then the only hit service advisor group is the optimal service advisor group.
Further, the invoking of the preset service advisor group filtering rule polls the service state of each service advisor group in the service advisor team matched with the target allocation rule in an inter-group polling manner, matches the service state of each service advisor group with the preset service advisor group filtering rule, and takes the service advisor group having the best service state and hitting the preset service advisor group filtering rule as the optimal service advisor group, further comprising:
if the total number of the hit service advisor group is not equal to 1, further judging whether the total number of the hit service advisor group is greater than 1;
if the total number of the hit service consultant groups is larger than 1, acquiring the last time-allocated customer service receiving time of each hit service consultant group;
comparing the last time of receiving service of different service consultant groups, selecting the service consultant group with the largest time difference between the current time and the last time of receiving service as the optimal service consultant group.
Further, the calling a preset service advisor filter rule, polling the service status of each service advisor in the optimal service advisor group in an in-group polling manner, matching the service status of each service advisor group with the preset service advisor group filter rule, and selecting the service advisor having the best service status and hitting the preset service advisor filter rule as the optimal service advisor comprises:
selecting a service advisor from the optimal service advisor group;
selecting the today's attendance amount, the attendance state and the last time-allocated attendance time of the service consultant from the service resource pool by taking the service consultant unique identifier as an index;
screening out the service consultants with the client receiving states as client receiving service consultants;
calculating the service weight of each hit service advisor according to formula 1;
Wi＝KA×Ai+KB×(B0-Bi) Formula 1;
wherein i is the service advisor unique identifier of the hit service advisor, WiA service weight that hits the service advisor. KAFor the preset weight of the present passenger capacity, AiTo hit the service advisor's present attendance, KBA preset weight for last allocation of pick-up time, B0As the current time, BiLast assigned pick-up time for the service advisor to hit;
comparing the service weights of the different service consultants, and using the service consultant corresponding to the maximum service weight as the optimal service consultant.
The present application further provides a system for allocating service resources, including:
an allocation terminal for performing the allocation method of service resources according to any one of claims 1 to 9;
and the database server is in communication connection with the distribution terminal and comprises a service resource pool.
On one hand, the method and the system can intelligently allocate the advisor service resources to the user in real time, so that the requirements of the user are matched with the characteristics of the advisor service resources, the allocation requirements of the existing insurance scene are fully met, and the timeliness is ensured. On the other hand, by means of automatic matching of the preset distribution rules, business personnel only need to pre-configure some preset distribution rules to input the distribution models to train the distribution models, manual participation is not needed in the actual service resource distribution process, errors caused by human factors can be reduced, and accuracy of service resource distribution is improved.
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Fig. 1 is a flowchart illustrating a method for allocating service resources according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a system for allocating service resources according to an embodiment of the present application.
Detailed Description
For the purpose of making the present application more apparent, technical solutions and advantages thereof are described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The application provides a method for allocating service resources. It should be noted that the method for allocating service resources provided by the present application can be applied to services such as policy allocation and insurance consultation in the insurance industry.
In addition, the method for allocating service resources provided by the application is not limited to the execution subject. Optionally, an execution subject of the service resource allocation method provided by the present application may be an allocation terminal. Specifically, an execution subject of the service resource allocation method provided by the present application may be one or more processors in the allocation terminal.
As shown in fig. 1, in an embodiment of the present application, the method for allocating service resources includes the following steps S100 to S700:
s100, obtaining the unique identification code of the user terminal, the user behavior data and the user personalized data.
Specifically, each ue has a unique ue identity, which is an IMEI code. When a user operates a user terminal to start an application program provided by the service platform of the my party on the user terminal, user traffic is input into the distribution terminal. The distribution terminal can obtain the unique identification code of the user terminal from the user terminal in real time at the moment.
The operations of the user terminal such as attention, consultation, private letter and the like on the public platform trigger the distribution terminal to acquire user behavior data. The collection of the user personalized data is triggered by the action that the user terminal actively clicks a push link on a public platform or a link corresponding to a menu bar button submits information, such as registering an account.
The user behavior data and the user personalized data are collected and stored in the database server, and are stored corresponding to the unique identification code of the user terminal, so that a coupling relation exists.
In this step S100, when the user traffic is sent to the distribution terminal, the distribution terminal may obtain the unique identification code of the user terminal from the user terminal in real time, and then call the user behavior data and the user personalized data corresponding to the unique identification code of the user terminal from the database server according to the unique identification code of the user terminal.
S200, judging whether the user terminal is a new user terminal according to the unique identification code of the user terminal.
Specifically, if the ue is not a new ue, it indicates that the allocation of the service resource has been performed for the ue, and the subsequent steps are terminated without repeated allocation.
This step can prevent repeated distribution and avoid wasting the calculation power of the distribution terminal.
And S300, if the user terminal is a new user terminal, analyzing the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal according to the user behavior data and the user personalized data of the user terminal.
Specifically, the service scenario feature attribute is a feature attribute related to a service scenario requirement. The self-demand attribute is a characteristic attribute relating to the user's self demand.
S400, inputting the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal into the distribution model.
In particular, the assignment model is a machine learning model, which is pre-trained.
And S500, operating the distribution model to obtain a preset distribution rule matched with the user terminal.
Specifically, the distribution model may match the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal with a plurality of preset distribution rules, and finally obtain a unique preset distribution rule matched with the user terminal.
The preset allocation rule is an allocation rule generated in advance by the allocation terminal according to different differentiated service scene characteristic attributes and self-demand characteristic attributes counted by big data and by combining the advisor service resources of the local service platform. There are a plurality of preset allocation rules.
When the distribution model is trained, a plurality of preset distribution rules and a large number of collected differentiated different business scene characteristic attributes and self-demand characteristic attributes are used as training data to be input into the distribution model for training, so that the distribution model has the most adaptive preset distribution rules which can be automatically matched according to the business scene characteristic attributes and the self-demand characteristic attributes.
For example, one preset allocation rule is:
1) the user gender tag is female.
2) The traffic scenario comes from information collection.
3) The amount of the budget of the bottom line of the user is in the range of the amount which is more than or equal to 0 and less than or equal to 1000 yuan, and the preset allocation rule is hit only if any two conditions are met.
It can be seen that 2) of the example preset allocation rules relate to service scenario feature attributes, and 1) and 3) relate to preset allocation rules.
S600, screening out the optimal service consultant from the service resource pool according to the preset distribution rule matched with the user terminal.
Specifically, there are multiple service consultants, and in this step, the optimal service consultant is screened out from the service resource pool according to the preset allocation rule matched with the user terminal.
S700, the user terminal and the service terminal corresponding to the optimal service consultant are established communication connection.
Specifically, after the optimal service advisor is screened out, the distribution terminal opens a communication interface between the user terminal and the service terminal corresponding to the optimal service advisor, so that the user terminal and the service terminal corresponding to the optimal service advisor directly establish communication connection, thereby facilitating subsequent service operation. .
On one hand, the method for allocating service resources provided by this embodiment can intelligently allocate advisor service resources to the user in real time, so that the requirements of the user are matched with the characteristics of the advisor service resources, thereby fully meeting the allocation requirements of the existing insurance scenarios and ensuring timeliness. On the other hand, by means of automatic matching of the preset distribution rules, business personnel only need to pre-configure some preset distribution rules to input the distribution models to train the distribution models, manual participation is not needed in the actual service resource distribution process, errors caused by human factors can be reduced, and accuracy of service resource distribution is improved.
In an embodiment of the present application, the user behavior data includes one or more of information collection data, public platform consultation data, and telemarketing data.
Specifically, the information collects data to include, but is not limited to, at least one of: the system comprises basic identity information data of a user, health information of each member of the user family members and historical participation information of each member of the user family members.
Public platform advisory data may include, but is not limited to, at least one of the following: consultation chatting records between users and public numbers under a certain public platform, and private letter chatting records between users and public platform management personnel under a certain public platform.
Telemarketing data may include, but is not limited to, at least one of: the customer service device communicates with the user through the telephone to know personal identity information data of the user, health information of each member of the family members of the user and historical participation information of each member of the family members of the user.
In the embodiment, the information collection data, the public platform consultation data and the telemarketing data can be acquired by triggering the acquisition operation through some active actions of the user terminal, the acquired data can be stored in the database server, and the data can be conveniently called in the follow-up process, so that the time is saved.
In an embodiment of the present application, the S200 includes the following S210 to S220:
s210, using the unique identification code of the user terminal as an index, searching the unique identification code of the service consultant corresponding to the unique identification code of the user terminal in the database server, and judging whether the unique identification code of the service consultant corresponding to the unique identification code of the user terminal exists in the database server.
Specifically, the user terminal unique identifier is userID, and the service advisor unique identifier is empId.
S220, if the unique identification code of the service consultant corresponding to the unique identification code of the user terminal does not exist in the database server, the user terminal is determined to be a new user terminal.
Specifically, if the service advisor unique identification code corresponding to the user terminal unique identification code does not exist in the database server, it is determined that the userID is not bound to the empId, and it is determined that the user terminal is a new user terminal.
Otherwise, if the service consultant unique identification code corresponding to the user terminal unique identification code exists in the database server, determining that the userID is already bound with the empId, and determining that the user terminal is not a new user terminal and is an old user terminal.
The embodiment can prevent the repeated allocation of the service resources and avoid the waste of the calculation power of the allocation terminal.
In an embodiment of the present application, the S500 includes the following S510 to S560:
and S510, operating the distribution model, and reading distribution type parameters of the distribution executors in the distribution model.
S520, acquiring N preset distribution rules corresponding to the distribution type parameters from the database server.
And S530, clearing the current data recorded by the counter.
S540, selecting a preset allocation rule from the N preset allocation rules.
And S550, judging whether the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal hit the preset distribution rule.
And S560, if the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal hit the preset distribution rule, taking the preset distribution rule as the preset distribution rule matched with the user terminal.
Specifically, each allocation type parameter corresponds to an allocation type. Each allocation type comprises a plurality of preset allocation rules.
The allocation types may include a dispatch allocation type, a traffic tier type, a customer allocation type, a telemarketing type, and so forth.
For example, the allocation type parameter is a scheduling allocation type, there are 10 preset allocation rules (N is 10) under the scheduling allocation type, and one preset allocation rule a under the scheduling allocation type is extracted as: 1) the user gender label is female, 2) the service scene comes from information collection, 3) the amount of the budget of the user bottom line is in the amount range of more than or equal to 0 and less than or equal to 1000 yuan, and the preset allocation rule is hit only if any two conditions are met.
And then the control distribution model judges whether the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal hit the preset distribution rule or not.
The service scene characteristic attribute of the user terminal is as follows: the service scene is information collection. The user terminal has the following characteristic attributes of self requirements: the sex is female, the amount of the bottom line budget of the user is in the range of more than or equal to 0 and less than or equal to 2000 yuan.
It can be understood that the service scenario characteristic attribute and the self-requirement characteristic attribute of the user terminal hit the preset allocation rule a, and then the preset allocation rule a is the preset allocation rule matched with the user terminal.
The general expansibility of the allocation mode in the embodiment is obviously improved compared with the traditional scheme. Different service scenes and user personalized characteristic attributes adopt different distribution modes, distribution types can be changed after matching is not performed, other services cannot be influenced when the service scenes are newly or changed every time, universal data input and universal data output cannot be influenced, and logic isolation on the services is achieved.
In an embodiment of the present application, the S500 further includes the following S571 to S575:
s571, if the service scenario characteristic attribute and the self-demand characteristic attribute of the user terminal do not hit the preset allocation rule.
And S572, adding 1 to the current data recorded by the counter on the basis of the original data.
And S573, judging whether the current data recorded by the counter is less than N.
And S574, if the current data recorded by the counter is less than N, returning to the S540.
And S575, if the current data recorded by the counter is greater than or equal to N, replacing the distribution type parameter of the distribution executor, and returning to the S510.
Specifically, if the current data recorded by the counter is less than N, the step returns to S540 to replace a preset allocation rule to continue the step of determining whether the preset allocation rule is hit.
If the current data recorded by the counter is greater than or equal to N, it indicates that all preset allocation rules under the allocation type are not hit by the user terminal, at this time, the allocation type parameter representing the allocation actuator is not appropriate, the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal cannot hit all the preset allocation rules under the allocation type parameter, and another different allocation type needs to be replaced.
In an embodiment of the present application, the S600 includes the following S610 to S640:
s610, the preset distribution rule matched with the user terminal is used as a target distribution rule.
Specifically, the target allocation rule is the preset allocation rule matching the user terminal obtained in the foregoing step S500.
S620, acquiring a service consultant team matched with the target distribution rule from the service resource pool. Each service advisor team includes a plurality of service advisor groups.
Specifically, the distribution terminal may assign a service advisor team to match for each preset distribution rule in advance. Each service advisor team may match a number of preset allocation rules. And the distribution terminal can adjust the preset distribution rules and the distribution of the service consultant team in real time according to the attendance condition, attendance arrangement, temporary personnel transfer condition, follow-up user condition and the like of different service consultant teams at preset time intervals.
For example, the 4-month 20-day service advisor team A matches the preset allocation rule A, the preset allocation rule B, the preset allocation rule C, and the preset allocation rule D. The service consultant team A asks for 3 people on leave in 4 months and 21 days, so that the distribution is adjusted in real time, after adjustment, the service consultant team A is matched with the preset distribution rule A and the preset distribution rule B, and the preset distribution rule C and the preset distribution rule D are handed to the service consultant team B for matching.
In this step S620, the distribution terminal obtains the service consultant team corresponding to the target distribution rule at the current time, and the timeliness is very strong.
S630, calling preset service consultant group filtering rules, polling the service state of each service consultant group in the service consultant team matched with the target allocation rules in an intergroup polling mode, matching the service state of each service consultant group with the preset service consultant group filtering rules, and taking the service consultant group which hits the preset service consultant group filtering rules and has the optimal service state as the optimal service consultant group.
Specifically, the counselor members of the service counselor team need to be grouped into different service counselor groups due to attendance arrangement, staff mobilization, follow-up user quality, and the like. Different service advisor groups have different service states.
Different members of the same service advisor group, i.e., different service advisors, may also have different service states.
In this embodiment, a service counselor group with the best service state is selected among the groups, and then a service counselor with the best service state is selected in the group.
In this step, the optimal service consultant is screened by presetting the filtering rules of the service consultant group, and the method is a polling method between groups.
S640, calling the preset service advisor filter rule, polling the service state of each service advisor in the optimal service advisor group in an in-group polling manner, matching the service state of each service advisor group with the preset service advisor group filter rule, and using the service advisor which hits the preset service advisor filter rule and has the optimal service state as the optimal service advisor.
Specifically, each service advisor group includes a plurality of service advisors, and different members of the same service advisor group, i.e., different service advisors, may have different service statuses.
In this step, the optimal service advisor is screened by pre-setting the service advisor filtering rules in an intra-group polling manner.
In an embodiment of the present application, S630 includes the following S631 to S638:
s631, selecting a service advisor group from the service advisor team matching the target allocation rule.
Specifically, after selecting the service counselor group, the service counselor group unique identifier of the service counselor group is obtained. Each service advisor group has its corresponding service advisor group unique identification, such as 40001,40002 or the like.
S632, obtaining the service counselor group receiving type, receiving amount, receiving time interval and receiving state from the service resource pool by using the service counselor group unique identification as index.
Specifically, the type of the pickup includes three types of timing, quantitative, timing, and quantitative. The receiving capacity is specifically the current daily receiving capacity, which expresses the number of users who have completed service today, for example, the current daily receiving capacity is 50, which represents that the service advisor group has completed service work for 50 user terminals. The service-receiving time interval is a time period expressed in terms of a time interval that the service advisor group can service. The pickup state includes two states of yes and no.
Optionally, after S632 and before S633, the method further includes:
acquiring the daily receiving capacity upper limit value of the service advisor group, and judging whether the current daily receiving capacity of the service advisor group is less than the daily receiving capacity upper limit value of the service advisor group.
If the current daily passenger volume of the service advisor group is less than the daily passenger volume upper limit value of the service advisor group, the following step S633 is continuously performed.
If the current daily volume of the service advisor group is equal to or greater than the upper limit of the daily volume of the service advisor group, the subsequent steps are interrupted, and S631 is returned to reselect another service advisor group.
And S633, calling a preset service advisor group filtering rule, and matching each of the type of the received call, the volume of the received call, the time interval of the received call and the state of the received call with the preset service advisor group filtering rule.
S634, determine whether each of the type, volume, time interval and status of the received service hits the preset service advisor group filter rules.
S635, if each of the type of receiving, the amount of receiving, the time interval of receiving, and the state of receiving hits the preset service counselor filtering rule, the service counselor is taken as a hit service counselor.
Specifically, for example, a preset service advisor group filter rule is 11:00 requires a request to allocate service resources, the pickup status is yes, and the current day pickup of the service advisor group is less than 70.
Assume that the service advisor groups in the service advisor team matching the target allocation rule have their attendance states as shown in table 1:
table 1-attendance status presentation table a for each service advisor group in the service advisor team matching the target allocation rule
Figure BDA0003619289230000161
In table 1, each service counselor has its own corresponding upper limit of the amount of received passengers, whether the amount of received passengers exceeds the upper limit of the amount of received passengers will be automatically reflected on the table, and if the amount of received passengers exceeds the upper limit of the amount of received passengers, whether the number of received passengers is listed is indicated as "no", otherwise, it is indicated as "yes".
From table 1, it can now be seen that:
the type of pickup of the service advisor group 40001 is timed, the current 11:00 does not satisfy the pickup time interval [12:00-18:00], and the service advisor group 40001 is not hit.
The service counselor group 40003 is timed and quantitative, 11:00 meets the receiving time interval, 30 meets the receiving condition (less than 70), and the receiving status (i.e., "whether the column can be received") is "yes", then the service counselor group 40003 is hit.
The service counselor group 40002 and the service counselor group 40004 have a fixed number of reception types, which means that reception is possible at any time, and the reception time interval is met, and the reception amount satisfies the condition of less than 70, but the reception status of both service counselor groups is not available, so that the service counselor group 40002 and the service counselor group 40004 are not hit.
When only one service advisor group is eligible, i.e., service advisor group 40003, the total number of hit service advisor groups equals 1, thus service advisor group 40003 acts as the optimal service advisor group.
And S636, returning to the S631 until all service advisor groups in the service advisor team matched with the target allocation rule are selected once, and obtaining at least one hit service advisor group.
Specifically, since there may be a plurality of service counselor groups hit, it is necessary to further select a unique optimal service counselor group.
S637, it is determined whether the total number of hit service advisor groups is equal to 1.
S638, if the total number of the hit service advisory groups is equal to 1, the only hit service advisory group is taken as the optimal service advisory group.
Specifically, if the total number of hit service advisor groups is equal to 1, then it is the best service advisor group. See table 1 for an example of an optimal service advisor group.
In this embodiment, the service state of the service advisor group is embodied according to the type of the service recipient, the amount of the service recipient, the time interval of the service recipient, and the status of the service recipient, which is relatively comprehensive and accurate. By judging whether each of the type of the received service, the amount of the received service, the time interval of the received service and the state of the received service hits the preset service advisor group filtering rule, the matching between the service advisor group and the user terminal can be optimized, and the user terminal is ensured to receive the service state with the optimal quality.
In an embodiment of the present application, the S630 further includes the following S639a to S639 c:
s639a, if the total number of the hit service advisor group is not equal to 1, further determine whether the total number of the hit service advisor group is greater than 1.
S639b, if the total number of the hit service counselor groups is greater than 1, the last assigned receiving time of each hit service counselor group is obtained.
S639c, compares the last time of receiving service allocated to different service counselor groups, and selects the service counselor group with the largest time difference between the current time and the last time of receiving service allocated to the service counselor group as the optimal service counselor group.
Specifically, if there are a plurality of hit service advisor groups, the present embodiment selects the hit service advisor group having the largest time difference between the current time and the last time of receiving service allocation as the optimal service advisor group.
I.e. the later the last time the pick-up time was allocated, the better the service status.
Referring to table 2, table 2 shows the case where the total number of hit service advisor groups is greater than 1.
In table 2, each service counselor has its own corresponding upper limit of the amount of received passengers, whether the amount of received passengers exceeds the upper limit of the amount of received passengers will be automatically reflected on the table, and if the amount of received passengers exceeds the upper limit of the amount of received passengers, whether the number of received passengers is listed is indicated as "no", otherwise, it is indicated as "yes".
TABLE 2-Subjects status of service advisor groups in service advisor team matching target assignment rules show TABLE B
Figure BDA0003619289230000181
If the preset service advisor group filter rule or current time is 11:00, a request for allocation of service resources is required, the pickup status is yes, and the current daily pickup of the service advisor group is less than 70.
From table 2 it can be derived:
the type of pickup of the service advisor group 40005 is timed, the current 11:00 does not satisfy the pickup time interval [12:00-18:00], and the service advisor group 40005 is not hit.
The type of service provider 40006 is quantitative, and meets the time interval of service provider (because the service provider can receive the service at any time), and the condition of the amount of service provider is satisfied, so that the service provider 40006 is hit.
The service counselor 40007 has a fixed time and fixed amount of service type, and meets the conditions of service time interval and service amount, so that the service counselor 40007 is hit.
The type of service advisor group 40008 is quantitative, and fits within the time interval of service (since it can be used at any time), but the service advisor group 40008 is unavailable and the service advisor group 40008 is not hit.
At this time, 2 service advisor groups are eligible, service advisor group 40006 and service advisor group 40007, and the total number of hit service advisor groups equals 2.
The service counselor group 40006 has a larger time difference between the current time of the service counselor group 40007 and the last assigned time to receive service than the service counselor group 40007, so that the service counselor group 40007 is the optimal service counselor group.
In this embodiment, when a plurality of service counselor groups are present, the service counselor group with the largest time difference between the current time and the last time is selected as the optimal service counselor group by comparing the last time allocation of the service counselor group, thereby averaging the working strength of each service counselor group, so as to make the allocation of the service counselor group more reasonable and avoid the service quality reduction caused by the fatigue of the individual service counselor group.
In an embodiment of the present application, the S640 includes the following S641 to S645:
s641, selecting a service advisor from the optimal service advisor group.
S642, selecting the today' S receiving capacity, receiving status and the last time-allocated receiving time of the service counselor from the service resource pool by taking the service counselor unique identification as an index.
S643, the service consultant whose receiving state is receivable is screened out as the hit service consultant.
S644, calculate the service weight of each hit service advisor according to equation 1.
Wi＝KA×Ai+KB×(B0-Bi) Equation 1.
Wherein i is the service advisor unique identifier of the hit service advisor, WiA service weight that hits the service advisor. KAFor the preset weight of the present passenger capacity, AiTo hit the service advisor's present attendance, KBA preset weight for last allocation of pick-up time, B0As the current time, BiThe last time the service advisor was hit was allocated the pick-up time.
S645, comparing the service weights of the different hit service advisors, and using the service advisor corresponding to the maximum service weight as the optimal service advisor.
Specifically, this embodiment is slightly different from the aforementioned method of screening the optimal service advisor group. Preset service handling of the present embodimentThe question filter rule is relatively single, and the variation may be the preset weight K of the present-day passenger quantity in the formula 1ADifferent preset weights K of last-time-allocated pick-up time of sumB。
For example, the preset weight K of the present amount of passengersATo-1, the last time the preset weight K of the time of pick-up was assignedBAt 5, then it can be seen that the greater the amount of customer that is received today, the more the service weight W that hits the service advisoriThe smaller the difference between the current time and the last time of the distributed pick-up time is, the larger the service weight is. Specific KAAnd KBThe value of (c) may be preset by the service personnel.
TABLE 3-display of the service advisor groups' status of the service advisors in the optimal service advisor group
Figure BDA0003619289230000201
Setting the current time as 12: 00.
for example, a predetermined weight K of the amount of passengers todayATo-1, the last time the preset weight K of the time of pick-up was assignedBTo 5, the service weight of each hitting service advisor is calculated separately:
the service advisor 50011 receives the least amount of customers today in the group, receives the customers in the receiving status, and last time allocation is 8 o' clock, and finally calculates the service weight value (-1) × 10+5 × (12-8) ═ 20-10, that is, 10.
The service advisor 50012, which receives 50 customers today, is not the least in the group, receives the customer in the receiving state, last time is 9 o' clock, and finally calculates the service weight as (-1) × 50+5 × (12-9) ═ 15-50, that is-35.
Advisor 50013, today's capacity 50, is not minimal in the group and is currently in a non-accessible state and therefore has a service weight of 0 because it is not a hit service advisor and therefore does not calculate a service weight. .
The final result is that service advisor 50011 has the highest service weight, and the best service advisor is service advisor 50011.
In this embodiment, the preset weight of the present customer service amount and the preset weight of the last time of the customer service allocation are set to define the service weight of the service advisor, so that the allocation terminal can more flexibly designate the allocation policy of the service advisor, and the automated allocation method of the service resource of the present application is more suitable for the actual situation.
In an embodiment of the present application, after the S700, the method for allocating service resources further includes:
s800, binding the service consultant unique identification code of the optimal service consultant with the user terminal unique identification code.
Specifically, after the binding, at the judgment of S200, the ue may be determined as an old ue, not a new ue.
The application also provides a system for distributing the service resources.
As shown in fig. 2, in an embodiment of the present application, a system for allocating service resources includes:
an allocation terminal 100 for performing the method of allocating service resources as mentioned in the foregoing.
And a database server 200 in communication connection with the distribution terminal 100, wherein the database server 200 comprises a service resource pool.
Specifically, in order to make the lines concise, the allocation terminal 100 and the database 200 are only numbered in this embodiment, and the various embodiments of the foregoing service resource allocation method are not numbered. The distribution terminal 100 obtains the unique identification code of the user terminal, the user behavior data and the user personalized data through the interaction between the user terminal and the application.
The technical features of the embodiments described above may be arbitrarily combined, the order of execution of the method steps is not limited, and for simplicity of description, all possible combinations of the technical features in the embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the combinations of the technical features should be considered as the scope of the present description.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims (10)

1. A method for allocating service resources, the method comprising:
acquiring a unique identification code of a user terminal, user behavior data and user personalized data;
judging whether the user terminal is a new user terminal or not according to the unique identification code of the user terminal;
if the user terminal is a new user terminal, analyzing the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal according to the user behavior data and the user personalized data of the user terminal;
inputting the service scene characteristic attribute and the self-demand characteristic attribute of the user terminal into the distribution model;
operating the distribution model to obtain a preset distribution rule matched with the user terminal;
screening out an optimal service consultant from the service resource pool according to a preset allocation rule matched with the user terminal;
and establishing communication connection between the user terminal and the service terminal corresponding to the optimal service consultant.
2. The method of claim 1, wherein the user behavior data comprises one or more of information gathering data, public platform consultation data, and telemarketing data.
3. The method according to claim 1, wherein the determining whether the ue is a new ue according to the unique ue identity comprises:
searching a service consultant unique identification code corresponding to the user terminal unique identification code in the database server by taking the user terminal unique identification code as an index, and judging whether the service consultant unique identification code corresponding to the user terminal unique identification code exists in the database server or not;
and if the unique service advisor identification code corresponding to the unique user terminal identification code does not exist in the database server, determining that the user terminal is a new user terminal.
4. The method according to claim 1, wherein the operating the allocation model to obtain the preset allocation rule matching with the ue comprises:
operating the distribution model, and reading the distribution type parameters of the distribution actuators in the distribution model;
acquiring N preset distribution rules corresponding to the distribution type parameters from a database server;
clearing the current data recorded by the counter;
selecting a preset distribution rule from the N preset distribution rules;
judging whether the service scene characteristic attribute and the self demand characteristic attribute of the user terminal hit the preset distribution rule or not;
and if the service scene characteristic attribute and the self demand characteristic attribute of the user terminal hit the preset distribution rule, taking the preset distribution rule as the preset distribution rule matched with the user terminal.
5. The method according to claim 4, wherein the operating the allocation model to obtain the preset allocation rule matching with the user terminal further comprises:
if the service scene characteristic attribute and the self demand characteristic attribute of the user terminal do not hit the preset distribution rule, adding 1 to the current data recorded by the counter on the basis of the original data;
judging whether the current data recorded by the counter is less than N;
if the current data recorded by the counter is less than N, returning to the step of selecting one preset distribution rule from the N preset distribution rules;
and if the current data recorded by the counter is greater than or equal to N, replacing the distribution type parameter of the distribution actuator, returning to the operation distribution model, and reading the distribution type parameter of the distribution actuator in the distribution model.
6. The method of claim 5, wherein the step of screening out the best service advisor from the service resource pool according to the preset allocation rule matching with the user terminal comprises:
taking a preset distribution rule matched with the user terminal as a target distribution rule;
acquiring a service consultant team matched with the target distribution rule from the service resource pool; each service advisor team comprising a plurality of service advisor groups;
calling preset service advisor group filtering rules, polling the service state of each service advisor group in the service advisor team matched with the target distribution rule in an intergroup polling mode, matching the service state of each service advisor group with the preset service advisor group filtering rules, and taking the service advisor group which hits the preset service advisor group filtering rules and has the optimal service state as an optimal service advisor group;
calling preset service advisor filter rules, polling the service state of each service advisor in the optimal service advisor group in an in-group polling manner, matching the service state of each service advisor group with the preset service advisor group filter rules, and taking the service advisor which hits the preset service advisor filter rules and has the optimal service state as the optimal service advisor.
7. The method of claim 6, wherein the invoking of the preset service advisor group filter rule polls the service status of each service advisor group in the service advisor team matched with the target allocation rule in an intergroup polling manner, matches the service status of each service advisor group with the preset service advisor group filter rule, and takes the service advisor group with the best service status and hit the preset service advisor group filter rule as the best service advisor group, comprises:
selecting a service advisor group from the service advisor team matching the target allocation rule;
acquiring the type, the volume, the time interval and the state of the service consultant from the service resource pool by taking the unique identifier of the service consultant group as an index;
calling a preset service advisor group filter rule, and matching each of the type of the received service, the volume of the received service, the time interval of the received service and the state of the received service with the preset service advisor group filter rule;
judging whether each of the type of the received call, the volume of the received call, the time interval of the received call and the state of the received call hits a preset service advisor group filtering rule;
if each of the type of receiving, the amount of receiving, the time interval of receiving and the state of receiving hits the preset service advisor group filtering rules, then the service advisor group is used as a hit service advisor group;
returning to the step of obtaining a service advisor group from the service resource pool by taking the service advisor group unique identifier as an index, and obtaining the type of receiving, the receiving capacity, the receiving time interval and the receiving state of the service advisor group until all service advisor groups in the service advisor team matched with the target distribution rule are selected once to obtain at least one hit service advisor group;
determining whether the total number of hit service advisor groups is equal to 1;
if the total number of hit service advisor groups is equal to 1, then the only hit service advisor group is the optimal service advisor group.
8. The method of claim 7, wherein the invoking of the preset service advisor group filter rule polls the service status of each service advisor group in the service advisor team matched with the target allocation rule in an intergroup polling manner, matches the service status of each service advisor group with the preset service advisor group filter rule, and takes the service advisor group with the best service status and hit the preset service advisor group filter rule as the best service advisor group, further comprising:
if the total number of the hit service advisor group is not equal to 1, further judging whether the total number of the hit service advisor group is greater than 1;
if the total number of the hit service consultant groups is larger than 1, acquiring the last time-allocated customer service receiving time of each hit service consultant group;
comparing the last time of receiving service of different service consultant groups, selecting the service consultant group with the largest time difference between the current time and the last time of receiving service as the optimal service consultant group.
9. The method of claim 8, wherein the step of calling the preset service advisor filter rule, polling the service status of each service advisor in the optimal service advisor group by in-group polling, matching the service status of each service advisor group with the preset service advisor group filter rule, and using the service advisor having the best service status and hitting the preset service advisor filter rule as the optimal service advisor comprises:
selecting a service advisor from the optimal service advisor group;
selecting the today's attendance amount, the attendance state and the last time-allocated attendance time of the service consultant from the service resource pool by taking the service consultant unique identifier as an index;
screening out the service consultants with the client receiving states as client receiving service consultants;
calculating the service weight of each hit service advisor according to formula 1;
Wi＝KA×Ai+KB×(B0-Bi) Formula 1;
wherein i is the service advisor unique identifier of the hit service advisor, WiTo hit the service advisor's service weight, KAFor the preset weight of the present passenger capacity, AiTo hit the service advisor's present attendance, KBPresetting right for last time distributing pick-up timeHeavy, B0As the current time, BiLast allotted pickup time for the hit service advisor;
comparing the service weights of the different hit service consultants, and using the service consultant corresponding to the maximum service weight as the optimal service consultant.
10. A system for allocating service resources, comprising:
at least one user terminal;
an allocation terminal, communicatively connected to each user terminal, for executing the allocation method of service resources according to any one of claims 1 to 9;
and the database server is in communication connection with the distribution terminal and comprises a service resource pool.
CN202210452409.1A 2022-04-27 2022-04-27 Service resource allocation method and system Pending CN114757430A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116523218A (en) * 2023-04-11 2023-08-01 深圳微应科技有限公司 Service personnel matching method and system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116523218A (en) * 2023-04-11 2023-08-01 深圳微应科技有限公司 Service personnel matching method and system

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