CN116090735A

CN116090735A - Customer data processing method and device and related equipment

Info

Publication number: CN116090735A
Application number: CN202211488501.XA
Authority: CN
Inventors: 曾文兵
Original assignee: Shenzhen Ideamake Software Technology Co Ltd
Current assignee: Shenzhen Ideamake Software Technology Co Ltd
Priority date: 2022-11-25
Filing date: 2022-11-25
Publication date: 2023-05-09

Abstract

The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing client data, and related devices. The method comprises the following steps: and acquiring a client data distribution mode, client data screening conditions and N1 pieces of service object data to be distributed, wherein N1 is a positive integer greater than or equal to 1. N2 target client data are determined from the client data resource pool according to the client data screening condition, wherein N2 is a positive integer greater than or equal to 1. And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode. By adopting the method, the efficiency of client allocation can be improved, and the rationality of client allocation is ensured.

Description

Customer data processing method and device and related equipment

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for processing client data, and related devices.

Background

With the rapid development of socioeconomic performance, the real estate industry has also gradually developed. At present, the real estate industry is provided with consultants, rationality comments are given according to the requirements of customers when the customers look at the houses and purchase the houses, the customers are helped to better select houses and purchase the houses, and the customers are helped to purchase the houses.

In existing customer allocation schemes, it is often necessary for a floor project manager to manually process and allocate customers to consultants. Because the customer distribution is required to be completed manually, the existing customer distribution scheme has the problems of low customer distribution efficiency, unreasonable customer distribution and the like. Therefore, how to quickly and reasonably distribute clients has become one of the technical problems to be solved.

Disclosure of Invention

The embodiment of the application provides a client data processing method, a device and related equipment, wherein a terminal device can establish a binding relationship between target client data and service object data to be distributed by acquiring a client data distribution mode, client data screening conditions and the service object data to be distributed, so that the distribution of clients can be completed rapidly and reasonably.

In a first aspect, an embodiment of the present invention provides a method for processing client data. The method comprises the following steps: and acquiring a client data distribution mode, client data screening conditions and N1 pieces of service object data to be distributed, wherein N1 is a positive integer greater than or equal to 1. And determining N2 target client data from the client data resource pool according to the client data screening conditions, wherein N2 is a positive integer greater than or equal to 1. And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode.

In the embodiment of the present application, the terminal device may determine N2 target client data from the client data resource pool through the acquired client data filtering condition, and then the terminal device may establish a binding relationship between each target client data in the N2 target client data and one service object data to be allocated in the acquired N1 service object data to be allocated according to the acquired data allocation mode. By adopting the method, the terminal equipment can realize the rapid distribution and reasonable distribution of the client data, thereby improving the efficiency of client distribution and ensuring the rationality of client distribution.

With reference to the first aspect, in a possible implementation manner, the acquiring N1 service object data to be allocated includes: and acquiring preset screening conditions of the service object data to be distributed. And screening N1 pieces of service object data to be distributed from a service object resource pool according to the service object data screening conditions to be distributed, wherein each piece of service object data to be distributed in the N1 pieces of service object data to be distributed meets the service object data screening conditions to be distributed.

With reference to the first aspect, in a possible implementation manner, the client data allocation mode is a priority allocation mode, N2 is equal to 1, and the establishing, according to the client data allocation mode, a binding relationship between each target client data of the N2 target client data and one service object data to be allocated of the N1 service object data to be allocated includes: determining a first priority of each piece of service object data to be distributed according to characteristic information contained in each piece of service object data to be distributed in the N1 pieces of service object data to be distributed through a preset first machine learning model, wherein the characteristic information contained in the service object data to be distributed comprises one or more of historical success rate, customer score or idle time contained in the service object data to be distributed. And determining the service object data to be distributed with the highest first priority according to the first priority of each service object data to be distributed. And establishing a binding relation between the target client data and the service object data to be distributed with the highest first priority.

With reference to the first aspect, in a possible implementation manner, the client data allocation mode is a priority allocation mode, N2 is greater than 1, and the establishing, according to the client data allocation mode, a binding relationship between each target client data of the N2 target client data and one service object data to be allocated of the N1 service object data to be allocated includes: determining a first priority of each piece of service object data to be distributed according to characteristic information contained in each piece of service object data to be distributed in the N1 pieces of service object data to be distributed through a preset first machine learning model, wherein the characteristic information contained in the service object data to be distributed comprises one or more of historical success rate, customer score or idle time contained in the service object data to be distributed. Determining, by a preset second machine learning model, a second priority of each target client data according to feature information included in each target client data in the N2 target client data, where the feature information included in the target client data includes one or more of a client source, a client purchase record, a client age, or a client purchase requirement included in the target client data. And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the second priority of each target client data and the first priority of each service object data to be distributed.

With reference to the first aspect, in a possible implementation manner, N2 is equal to N1, and the establishing, according to the second priority of each target client data and the first priority of each service object data to be allocated, a binding relationship between each target client data of the N2 target client data and one service object data to be allocated of the N1 service object data to be allocated includes: executing the following binding operation on any first target client data in the N2 target client data: and determining first to-be-allocated service object data corresponding to the first target client data from the N1 to-be-allocated service object data, wherein the second priority of the first target client data is equal to the first priority of the first to-be-allocated service object data. And establishing a binding relation between the first target client data and the first to-be-distributed service object data. And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the result of executing the binding operation on each target client data in the N2 target client data.

With reference to the first aspect, in a possible implementation manner, the method further includes: acquiring identity information contained in any second target client data of the N2 target client data and identity information contained in second service object data to be distributed in the N1 service object data to be distributed, and determining the identity information contained in the second target client data and the identity information contained in the second service object data to be distributed as target log data, wherein a binding relationship exists between the second target client data and the second service object data to be distributed. And storing the target log data into a preset log database.

With reference to the first aspect, in a possible implementation manner, after the establishing a binding relationship between each target client data of the N2 target client data and one service object to be allocated of the N1 service object to be allocated data according to the client data allocation mode, the method further includes: executing the following service information recommendation operations on the target clients corresponding to any third target client data in the N2 target client data: and generating service recommendation information according to the third service object data to be distributed of the third target client data, wherein the service recommendation information comprises identity information contained in the third service object data to be distributed and/or service resources contained in the third service object data to be distributed. And pushing the service recommendation information to the target client corresponding to the third target client data.

In a second aspect, an embodiment of the present invention provides a client data processing apparatus. The device comprises: and the acquisition unit is used for acquiring the client data distribution mode, the client data screening condition and N1 pieces of service object data to be distributed, wherein N1 is a positive integer greater than or equal to 1. And the processing unit is used for determining N2 target client data from the client data resource pool according to the client screening conditions, wherein N2 is a positive integer greater than or equal to 1. And the binding unit is used for establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: and the acquisition unit is used for acquiring preset service object data screening conditions to be distributed. And the processing unit is used for screening N1 pieces of service object data to be distributed from the service object resource pool according to the service object data to be distributed, wherein each piece of service object data to be distributed in the N1 pieces of service object data to be distributed meets the screening condition of the service object data to be distributed.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: the processing unit is configured to determine, according to a preset first machine learning model, a first priority of each service object data to be allocated according to feature information included in each service object data to be allocated in the N1 service object data to be allocated, where the feature information included in the service object data to be allocated includes one or more of a historical success rate, a customer score, or an idle time included in the service object data to be allocated. And the processing unit is used for determining the service object data to be distributed with the highest first priority according to the first priority of each service object data to be distributed. And the binding unit is used for establishing a binding relation between the target client data and the service object data to be distributed with the highest first priority.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: the processing unit is configured to determine, according to a preset first machine learning model, a first priority of each service object data to be allocated according to feature information included in each service object data to be allocated in the N1 service object data to be allocated, where the feature information included in the service object data to be allocated includes one or more of a historical success rate, a customer score, or an idle time included in the service object data to be allocated. And the processing unit is used for determining the second priority of each target client data according to the characteristic information contained in each target client data in the N2 target client data through a preset second machine learning model, wherein the characteristic information contained in the target client data comprises one or more of a client source, a client purchase record, a client age or a client purchase requirement contained in the target client data. And the binding unit is used for establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the second priority of each target client data and the first priority of each service object data to be distributed.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: the processing unit is used for executing the following binding operation on any first target client data in the N2 target client data, and the specific operation content is as follows: and the processing unit is used for determining first to-be-allocated service object data corresponding to the first target client data from the N1 to-be-allocated service object data, wherein the second priority of the first target client data is equal to the first priority of the first to-be-allocated service object data. And the binding unit is used for establishing a binding relation between the first target client data and the first to-be-allocated service object data. And the processing unit is used for establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the result of executing the binding operation on each target client data in the N2 target client data.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: the processing unit is configured to obtain identity information included in any second target client data of the N2 target client data and identity information included in second service object data to be allocated in the N1 service object data to be allocated, and determine the identity information included in the second target client data and the identity information included in the second service object data to be allocated as target log data, where a binding relationship exists between the second target client data and the second service object data to be allocated. And the storage unit is used for storing the target log data into a preset log database.

With reference to the second aspect, in a possible implementation manner, the apparatus includes: the processing unit is used for executing the following service information recommendation operation on the target client corresponding to any third target client data in the N2 target client data, and the specific operation content is as follows: and the processing unit is used for generating service recommendation information according to the third service object data to be distributed of which the binding relation is established according to the third target client data, wherein the service recommendation information comprises identity information contained in the third service object data to be distributed and/or service resources contained in the third service object data to be distributed. And the pushing unit is used for pushing the service recommendation information to the target client corresponding to the third target client data.

In a third aspect, an embodiment of the present application provides a computer readable storage medium, where the computer readable storage medium is configured to store a computer program, where the computer program when executed on a computer causes the computer to execute a client data processing method provided by any one of the possible implementation manners of the first aspect, and also implements the beneficial effects provided by the client data processing method provided by the first aspect.

In a fourth aspect, embodiments of the present application provide an electronic device that may include a processor and a memory, the processor and the memory being interconnected. The memory is configured to store a computer program, and the processor is configured to execute the computer program to implement the client data processing method provided in the first aspect, and also implement the beneficial effects of the client data processing method provided in the first aspect.

By implementing the embodiment of the invention, the terminal equipment can establish a binding relation between each target client data in N2 target client data and one service object data to be distributed in N1 service object data to be distributed through a preset client data distribution mode, and the client data can be rapidly distributed and reasonably distributed through the preset client data distribution mode, so that the client distribution efficiency is improved, and the client distribution rationality is ensured.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for processing customer data according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a man-machine interface configured with client allocation rules according to an embodiment of the present application;

FIG. 3 is a table schematic diagram of a client data processing state provided in an embodiment of the present application;

FIG. 4 is a schematic flow chart of a client data processing method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a client data processing apparatus according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a client data processing apparatus according to an embodiment of the present application;

FIG. 7 is a schematic diagram of a client data processing apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.

The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps is not limited to the elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

Existing building project managers need to manually identify and assign customers to consultants. However, this results in that the manager spends a lot of time processing the distribution work, and thus the distribution work is inefficient. Meanwhile, the manager has the conditions of uneven distribution or unreasonable distribution to the clients, so that the user experience is poor. Therefore, the technical problem to be solved by the application is as follows: how to quickly and reasonably distribute customers.

It should be noted that the client data processing method provided by the application can be applied to the scenes of real estate transaction, automobile transaction and the like. For example, in an application scenario of a property transaction, a "customer" involved in an embodiment of the present application may be a property purchasing person, a "service object to be allocated" may be a service consultant, a "customer data" may include information such as a name, a telephone, and a purchase requirement of the property purchasing person, and a "service object to be allocated" may include information such as a name, a telephone, a historical yield, and a customer score of the service consultant. For another example, in an application scenario of a car transaction, the "customer" involved in the embodiment of the present application may be a car purchaser, the "service object to be allocated" may be a car sales advisor, the "customer data" may include information such as a name, a phone number, and a purchase requirement of the car purchaser, and the "service object data to be allocated" may include information such as a name, a phone number, a historical yield, and a customer score of the car sales advisor.

Referring to fig. 1, fig. 1 is a flow chart of a client data processing method according to an embodiment of the present application. The client data processing may be performed by the terminal device invoking its built-in client allocation engine. In the embodiment of the application, the terminal device may be any form of electronic device capable of performing client data processing, such as a smart phone, a portable notebook computer, a desktop computer, a self-service terminal, and the like. The implementation form of the terminal device is not particularly limited. As shown in fig. 1, the client data processing method specifically may include the steps of:

S101, acquiring a client data distribution mode, client data screening conditions and N1 service object data to be distributed.

In some possible embodiments, the terminal device may obtain the client data allocation mode, the client data filtering condition, and N1 service object data to be allocated. Wherein N1 is a positive integer greater than or equal to 1. Here, the client data distribution mode is a rule which is set in advance and is used for indicating how to establish a binding relationship between client data and service object data to be distributed. In embodiments of the present application, the client data allocation pattern may include a random allocation pattern, a polling allocation pattern, a performance rewards pattern, a priority allocation pattern, and the like.

In a specific implementation, the terminal device may perform the scanning operation of the client allocation rule at regular time by using the client allocation engine according to a preset timing task to obtain a preset allocation rule. Further, the terminal device analyzes the client allocation rule, and may obtain a client data allocation mode, a client data filtering condition, and N1 service object data to be allocated. It should be noted here that the preset client allocation rules may be modified by the user according to different requirements on the terminal device.

For example, it is assumed herein that the preset timed task is to perform the customer assignment rule scan operation every 10 minutes at intervals of 8:00 to 18:00, and to perform the customer assignment rule scan operation every 30 minutes at intervals of 18:00 to 21:00. In the range of 8:00-10: and 00, the terminal equipment executes the scanning operation of the client allocation rule according to the timing task every 10 minutes to acquire the preset client allocation rule. Further, the terminal device analyzes the client allocation rule, and may obtain a client data allocation mode, a client data filtering condition, and N1 service object data to be allocated. In a specific implementation, at the moment of 8:10, the terminal device executes a scanning operation of the client allocation rule to acquire a preset client allocation rule. Further, the terminal device analyzes the client allocation rule, and can obtain that the client data allocation mode is a random mode, the client data filtering condition is "clients who want to purchase a study district and clients who want to purchase a district with convenient traffic", and the N1 pieces of service object data to be allocated are service object data a to be allocated, service object data B to be allocated and service object data C to be allocated.

Alternatively, the customer allocation rules may be preconfigured by the user via a human-machine interaction interface. Referring to fig. 2, fig. 2 is a schematic diagram of a man-machine interaction interface configured by a client allocation rule according to an embodiment of the present application. As shown in fig. 2, the human-computer interaction interface includes four editable areas, namely, an editable area 1, an editable area 2, an editable area 3 and an editable area 4. The terminal device may determine that the content received by the editable area 1 and input by the user is a customer allocation rule name, determine that the content received by the editable area 2 and input by the user is a customer data filtering condition, determine that the content received by the editable area 3 and input by the user is N1 pieces of service object data to be allocated, and determine that the content received by the editable area 4 and input by the user is a customer data allocation mode. Then, the terminal device can determine a client allocation rule according to the client allocation rule name, the client data filtering condition, the N1 service object data to be allocated and the client data allocation mode.

It should be noted here that the preset timing tasks can be modified correspondingly on the terminal device by the manager according to different quarters and different market demands. For example, assuming that the manager is in a busy season of shopping, the manager may modify the originally preset timing task to one in which the scanning operation is more frequent. Specifically, it is assumed here that the timing task set by the original system is executed every 20 minutes at intervals of 8:00 to 18:00, and the allocation rule scanning operation is executed every 60 minutes at intervals of 18:00 to 21:00. Since the manager can modify the timing task to perform the allocation rule scanning operation every 10 minutes at intervals of 8:00-18:00 when the manager is in a house-buying and busy season, and to perform the allocation rule scanning operation every 30 minutes at intervals of 18:00-21:00.

In an alternative embodiment, the allocation rule may include N1 pieces of service object data to be allocated. The terminal device can acquire a preset allocation rule according to a preset timing task through the allocation engine. Further, the terminal device analyzes the allocation rule, and N1 service object data to be allocated can be obtained.

In another alternative embodiment, the allocation rule may include a service object data filtering condition to be allocated, in which case, the terminal device may first obtain a preset service object data filtering condition to be allocated. Further, the terminal device can screen out N1 service object data to be allocated from the preset service object resource pool according to the service object data screening condition to be allocated. Wherein, each of the N1 service object data to be allocated satisfies the service object data filtering condition to be allocated. Here, the preset service object data filtering condition to be allocated may include one or more service object data filtering conditions to be allocated. The service object resource pool may be a resource database containing all service object data. Specifically, the service object resource pool may include names, phones, sexes, historical transaction rates, service resources, and the like corresponding to each service object data in all the service object data.

For example, it is assumed here that the preset service object data screening condition to be allocated is "the service object to be allocated in charge of the area a and the area B". The terminal equipment can acquire preset screening conditions of the service object data to be distributed. Further, the terminal device may screen out a service object data a to be allocated from the service object resource pool according to the preset service object data screening condition to be allocated, where N1 is equal to 1.

S102, N2 target client data are determined from the client data resource pool according to the client data screening conditions.

In some possible embodiments, the terminal device may determine, according to the client data filtering condition, N2 target client data that satisfy the client data filtering condition from a preset client data resource pool. Here, N2 is a positive integer greater than or equal to 1.

For example, it is assumed herein that the customer data screening conditions include "customers who want to purchase a study area house" and "customers who want to purchase a traffic-friendly area". It is assumed that the preset client data resource pool includes target client data a, target client data B, target client data C, target client data D, and target client data E, where only the client requirement included in the target client data C is "want to purchase a study area house". The terminal device may screen out target client data C satisfying the client data screening condition from a preset client data resource pool according to the client data screening condition, and determine the client data of the target client data C as the N2 target client data, where N2 is equal to 1.

As another example, assume herein that the customer data filtering condition includes "customer data that has just been added recently in a preset customer data resource pool". The preset client data resource pool is assumed to contain target client data A, target client data B, target client data C, target client data D and target client data E, wherein the target client data B and the target client data C are client data which are added in the preset client data pool recently. The terminal device can screen out target client data B and target client data C meeting the client data screening conditions from a preset client data resource pool according to the client data screening conditions, and determine the target client data B and the target client data C as N2 target client data, wherein N2 is equal to 2.

In the embodiment of the present application, the client data screening condition may be other specific screening conditions, as long as the screening of the target client data can be achieved, which is not specifically limited in the embodiment of the present application.

S103, establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode.

In some possible embodiments, the terminal device may establish a binding relationship between each target client data in the N2 target client data and one service object data to be allocated in the N1 service object data to be allocated according to the client data allocation mode, so as to complete allocation of N2 target clients corresponding to the N2 target client data. It should be noted that, one target client data establishes a binding relationship with one service object data to be allocated, and one service object data to be allocated may have a binding relationship with a plurality of target client data.

In an optional implementation manner, when the acquired client data allocation mode is a priority allocation mode and N2 is equal to 1 (i.e. only one target client data), the terminal device may determine, according to the feature information included in each service object data to be allocated in the N1 service object data to be allocated, the first priority of each service object data to be allocated through a preset first machine learning model. Then, the terminal device can determine the service object data to be allocated with the highest first priority according to the first priority of each service object data to be allocated. Further, the terminal device may establish a binding relationship between the target client data and the service object data to be allocated with the highest first priority.

Here, the feature information included in the service object data to be distributed may include one or more of a historical transaction rate, a customer score, or an idle time included in the service object data to be distributed. For example, the feature information included in the service object data a to be distributed may include that the service object data a to be distributed includes a historical success rate of 90%. For another example, the feature information included in the service object data a to be distributed may include a historical success rate of 90%, a customer score of 9.0 (full score of 10.0), and an idle time of four hours.

It is here assumed, for example, that the terminal device determines a target client data as target client data a from the client data resource pool based on the client data filtering condition. Let N1 pieces of service object data to be allocated include service object data a to be allocated, service object data B to be allocated, service object data C to be allocated, and service object data D to be allocated, where N1 is equal to 4. The terminal device determines that the first priorities of the service object data a to be allocated, the service object data B to be allocated, the service object data C to be allocated and the service object data D to be allocated are 1, 3, 2 and 4 respectively according to the historical success rate, the customer score and the idle time contained in each service object data to be allocated in the 4 service object data to be allocated through a preset first machine learning model. Then, the terminal device can determine that the service object data to be allocated with the highest first priority is the service object data A to be allocated according to the first priority of each service object data to be allocated. Further, the terminal device may establish a binding relationship between the target client data a and the service object data a to be allocated.

Optionally, the preset first machine learning model is obtained by training the model. The terminal equipment can acquire a data set containing characteristic information parameters contained in the service object data to be distributed, marks the first priority of the service object data to be distributed, and divides the marked data set into a training set and a testing set according to a preset proportion. Then, the training set is input into a first machine learning model to train, and the terminal equipment can acquire the pre-training weight of the first machine learning model output by the model. In this embodiment of the present application, the preset first machine learning model may determine a first priority of service object data to be allocated corresponding to a characteristic information parameter included in the service object data to be allocated by using the pre-training weight.

In another optional implementation manner, when the acquired client data allocation mode is a priority allocation mode and N2 is greater than 1, the terminal device may determine, according to the feature information included in each service object data to be allocated in the N1 service object data to be allocated, a first priority of each service object data to be allocated through a preset first machine learning model. Then, the terminal device can determine the second priority of each of the N2 target client data according to the characteristic information contained in each of the N2 target client data through a preset second machine learning model. Further, the terminal device may establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the second priority of each target client data and the first priority of each service object data to be allocated.

Here, the characteristic information included in the target client data includes one or more of a client source, a client purchase record, a client age, and a client purchase requirement included in each target client data. For example, the characteristic information contained in the target customer data a may include that the target customer data a contains a customer purchase demand that is a purchase of a set of study rooms. For another example, the characteristic information included in the target customer data a may include that the target customer data a includes a customer age of 30 years, and a customer purchase demand is a desire to purchase a set of study rooms.

Optionally, the preset second machine learning model is obtained by performing model. The terminal equipment can acquire a data set comprising characteristic information parameters contained in the target client data, label the second priority of the target client data, and divide the labeled data set into a training set and a testing set according to a preset proportion. Then, the training set is input into a second machine learning model to train, and the terminal equipment can acquire the pre-training weight of the second machine learning model output by the model. In this embodiment of the present application, the second pre-set machine learning model may determine the second priority of the target client data by using the pre-training weights.

In a specific implementation, when N2 is equal to N1, the terminal device may determine, according to the feature information included in each service object data to be allocated in the N1 service object data to be allocated, a first priority of each service object data to be allocated through a preset first machine learning model, and the terminal device may determine, according to the feature information included in each target client data in the N2 target client data through a preset second machine learning model, a second priority of each target client data. Then, the terminal device may perform a binding operation on each of the N2 target client data according to the second priority of each of the target client data and the first priority of each of the service object data to be allocated, so as to establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated. The binding operation is described in an exemplary manner by taking any first target client data of the N2 target client data as an example. Specifically, the terminal device may determine, from the N1 to-be-allocated service object data, first to-be-allocated service object data corresponding to the first target client data. Here, the second priority of the first target client data is equal to the first priority of the first to-be-allocated service object data. Further, the terminal device may establish a binding relationship between the first target client data and the first object data to be allocated.

For example, here, it is assumed that N2 and N1 are equal to 3, that N1 service object data to be allocated includes service object data a to be allocated, service object data B to be allocated, and service object data C to be allocated, that N2 target client data include target client data a, target client data B, and target client data C, that the terminal device determines, according to feature information included in each of the 3 service object data to be allocated, that the first priorities of the service object data a to be allocated, the service object data B to be allocated, and the service object data C to be allocated are 1, 3, and 2, respectively, by means of a preset first machine learning model, and that the device determines, according to feature information included in each of the 3 target client data, that the second priorities of the target client data a, the target client data B, and the target client data C are 2, 1, and 3, respectively, by means of a preset second machine learning model. Then, the terminal device may determine, according to the second priority of each target client data and the first priority of each service object data to be allocated, that the second priority of the target client data a is equal to the first priority of the service object data B to be allocated, that the second priority of the target client data B is equal to the first priority of the service object data C to be allocated, and that the second priority of the target client data C is equal to the first priority of the service object data a to be allocated. Furthermore, the terminal device may respectively and correspondingly establish a binding relationship between the target client data a and the service object data B to be allocated, between the target client data B and the service object data C to be allocated, and between the target client data C and the service object data a to be allocated.

In a specific implementation, when N2 is smaller than N1, the terminal device may determine, according to the feature information included in each service object data to be allocated in the N1 service object data to be allocated, a first priority of each service object data to be allocated through a preset first machine learning model, and the terminal device may determine, according to the feature information included in each target client data in the N2 target client data, a second priority of each target client data through a preset second machine learning model. Further, the terminal device may perform a binding operation on each of the N2 target client data according to the second priority of each target client data and the first priority of each service object data to be allocated, so as to establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated. The binding operation is the same as the binding operation described above, and will not be described here. It should be noted that, in the N1 service object data to be allocated, N2 service object data to be allocated are corresponding to each target client data in the N2 target client data, and the remaining N1 to N2 service object data to be allocated are not processed temporarily.

For example, here, it is assumed that N2 is equal to 2, N1 is equal to 3, N1 service object data to be allocated includes service object data a to be allocated, service object data B to be allocated, and service object data C to be allocated, N2 target client data include target client data a and target client data B, it is assumed that the terminal device can determine that the first priorities of the service object data a to be allocated, the service object data B to be allocated, and the service object data C to be allocated are 2, 1, and 3, respectively, according to the characteristic information included in each of the 2 target client data by a preset first machine learning model, and it is assumed that the second priorities of the target client data a and the target client data B are 1 and 2, respectively, according to the characteristic information included in each of the 2 target client data by a preset second machine learning model. Then, the terminal device may determine, according to the second priority of each target client data and the first priority of each service object data to be allocated, that the second priority of the target client data a is equal to the first priority of the service object data B to be allocated, and that the second priority of the target client data B is equal to the first priority of the service object data a to be allocated. Furthermore, the terminal device may respectively and correspondingly establish a binding relationship between the target client data a and the service object data B to be allocated, and between the target client data B and the service object data a to be allocated, while the service object data C to be allocated is temporarily not processed.

In a specific implementation, when N2 is greater than N1, the terminal device may determine, according to a preset first machine learning model, a first priority of each service object data to be allocated according to feature information included in each service object data to be allocated in the N1 service object data to be allocated, and the terminal device may determine, according to a preset second machine learning model, a second priority of each target client data according to feature information included in each target client data in the N2 target client data. Further, the terminal device may perform a binding operation on each of the N2 target client data according to the second priority of each target client data and the first priority of each service object data to be allocated, and may establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated. The binding operation is the same as the binding operation described above, and will not be described here.

It should be noted that, because N2 is greater than N1, the terminal device may first establish a binding relationship between N1 target client data in the N2 target client data and one service object data to be allocated in the N1 service object data to be allocated according to the second priority of each target client data in the N2 target client data and the first priority of each service object data to be allocated in the N1 service object data to be allocated. Then, the remaining N2-N1 target client data can determine the third priority of each target client data through a preset second machine learning model. Here, it is assumed that N2-N1 is greater than N1. Then, the terminal device performs a binding operation on each client data of the N2-N1 target client data, so that a binding relationship can be established between N1 target client data of the N2-N1 target client data and one service object data to be allocated of the N1 service object data to be allocated. And so on, until each target client data in the N2 target client data is in binding relation with one service object data to be distributed in the N1 service object data to be distributed.

For example, it is assumed here that N2 is equal to 5, N1 is equal to 2, N1 service object data to be allocated includes service object data a to be allocated and service object data B to be allocated, N2 target client data include target client data a, target client data B, target client data C, target client data D, and target client data E, it is assumed that the terminal device can determine that the first priorities of the service object data a to be allocated and the service object data B to be allocated are 1 and 2 according to the feature information included in each of the 2 service object data to be allocated through a preset first machine learning model, and it is assumed that the terminal device can determine that the second priorities of the target client data a, the target client data B, the target client data C, the target client data D, and the target client data E are 3, 1, 4, 2, and 5 according to the feature information included in the 5 target client data through a preset second machine learning model. Then, the terminal device may determine, according to the second priority of each target client data and the first priority of each service object data to be allocated, that the second priority of the target client data B is equal to the first priority of the service object data a to be allocated, and that the second priority of the target client data D is equal to the first priority of the service object data B to be allocated. Furthermore, the terminal device may respectively and correspondingly establish a binding relationship between the target client data B and the service object data a to be allocated, and between the target client data D and the service object data B to be allocated. And the target client data A, the target client data B and the target client data E which are not processed are determined to be respectively 1, 2 and 3 according to the characteristic information contained in the three target client data through a preset second machine learning model. Then, the terminal device may determine, according to the third priorities of the three target client data and the first priorities of the service object data to be allocated, that the third priority of the target client data a is equal to the first priority of the service object data to be allocated, and that the third priority of the target client data B is equal to the first priority of the service object data to be allocated. Furthermore, the terminal device may respectively and correspondingly establish a binding relationship between the target client data a and the service object data a to be allocated, and between the target client data C and the service object data B to be allocated. And the remaining unprocessed target client data E may determine that the fourth priority of the target client data E is 1 through a preset second machine learning model. Then, the terminal device may determine, according to the fourth priority of the target client data E and the first priority of each service object data to be allocated, that the second priority of the target client data E is equal to the first priority of the service object data a to be allocated. Further, the terminal device may establish a binding relationship between the target client data E and the service object data a to be allocated. So far, the 5 target client data all complete the client allocation according to the priority allocation mode.

In an alternative embodiment, when the client data allocation mode is a random allocation mode, the terminal device may implement, by using a preset random algorithm, to establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated. It should be noted that, only one target client data can establish a binding relationship with one service object data to be allocated, while one service object data to be allocated may have a binding relationship with a plurality of target client data, and one service object data to be allocated may not have a binding relationship with the target client data.

For example, here, N2 is assumed to be equal to 5, N1 is assumed to be equal to 3, N2 pieces of target client data are assumed to include target client data a, target client data B, target client data C, target client data D, and target client data E, and N1 pieces of service object data to be allocated are assumed to include service object data a to be allocated, service object data B to be allocated, and service object data C to be allocated. The terminal device can respectively and correspondingly establish a binding relation between the target client data A and the service object data B to be distributed, between the target client data B and the service object data C to be distributed, between the target client data C and the service object data C to be distributed, between the target client data D and the service object data A to be distributed, and between the target client data E and the service object data B to be distributed through a preset random algorithm.

For another example, here, N2 is assumed to be 3, N1 is assumed to be 4, N1 target client data is assumed to include target client data a, target client data B, and target client data C, and N2 service objects to be allocated are assumed to include service object data a to be allocated, service object data B to be allocated, service object data C to be allocated, and service object data D to be allocated. The terminal device can respectively and correspondingly establish a binding relation between the target client data A and the service object data C to be distributed, between the target client data B and the service object data D to be distributed, and between the target client data C and the service object data A to be distributed through a preset random algorithm, wherein the service object data B to be distributed does not have the binding relation between the target client data and the service object data A to be distributed.

In an alternative embodiment, when the client data allocation mode is a polling allocation mode, the terminal device sequentially corresponds to each of the N2 target client data and one of the N1 service object data to be allocated according to the order of the target client data and the order of the service object data to be allocated.

It should be noted that, when N2 is greater than N1, after a binding relationship is established between each target client data in the first N1 target client data in the N2 target client data and one service object data to be allocated in the N1 service object data to be allocated, the remaining N2-N1 target client data still sequentially corresponds to the sequence of the service object data to be allocated in sequence, respectively, and a binding relationship is established between each target client data in the N2-N1 target client data and one service object data to be allocated in the N1 service object data to be allocated. And so on, until each client data in the N2 target client data is in binding relation with one service object data to be distributed in the N1 service object data to be distributed. Here, it is assumed that N2-N1 is greater than N1. When N2 is less than or equal to N1, the terminal device sequentially corresponds to the sequence of the target client data and the sequence of the service object data to be allocated, and establishes a binding relationship between each target client data in the N2 target client data and one service object data to be allocated in the N1 service object data to be allocated. Specifically, when N2 is smaller than N1, N1-N2 service object data to be distributed remain, and no target client data is established with the service object data to be distributed, and the service object data is not processed.

For example, here, N2 is assumed to be equal to 2, N1 is assumed to be equal to 3, the order of target client data contained in N2 target client data is assumed to be target client data a and target client data B, and the order of service objects to be allocated contained in N1 service object data to be allocated is assumed to be service object data a to be allocated, service object data B to be allocated, and service object data C to be allocated. The terminal device sequentially corresponds to the sequence of the target client data and the sequence of the service object data to be distributed, and can respectively correspondingly establish a binding relation between the target client data A and the service object data A to be distributed and between the target client data B and the service object data B to be distributed.

For another example, here, N2 is assumed to be 3, N1 is assumed to be 2, the order of the target client data included in the N2 target client data is assumed to be target client data a, target client data B, and target client data C, and the order of the service object data to be allocated included in the N1 service object data to be allocated is assumed to be service object data a to be allocated and service object data B to be allocated. The terminal device sequentially corresponds to the sequence of the target client data and the sequence of the service object data to be distributed, and can respectively correspondingly establish a binding relation between the target client data A and the service object data A to be distributed, between the target client data B and the service object data B to be distributed, and between the target client data C and the service object data A to be distributed.

In an optional embodiment, when the client data allocation mode is a performance rewards allocation mode, the terminal device may determine, according to performance achievement included in each service object data to be allocated in the N1 service object data to be allocated, a performance rewards sequence of each service object data to be allocated through a preset third machine learning model. And then, the terminal equipment can determine the service object data to be distributed with the top two service objects of the performance ranking according to the performance rewarding sequence of the service object data to be distributed. Furthermore, the terminal device can sequentially establish binding relations between 3 and 2 target client data in the N2 target client data and the two to-be-allocated service object data with the top performance ranking, and sequentially establish binding relations between one target client data in the N2 target client data and the rest to-be-allocated service object data according to the performance rewarding sequence.

For example, here, N2 is assumed to be equal to 7, N1 is assumed to be equal to 5, N2 pieces of target client data are assumed to include target client data a, target client data B, target client data C, target client data D, target client data E, target client data F, and target client data G, and N1 pieces of service object data to be allocated are assumed to include service object data a to be allocated, service object data B to be allocated, service object data C to be allocated, service object data D to be allocated, and service object data E to be allocated. The terminal device can determine that the performance rewarding sequence of the service object data to be distributed is the service object data to be distributed B, the service object data to be distributed A, the service object data to be distributed D, the service object data to be distributed C and the service object data to be distributed E according to the performance achievement contained in each service object data to be distributed in the 5 service object data to be distributed. Then, the terminal device can determine that the service object data to be allocated with the top two names of the performance ranks are the service object data to be allocated B and the service object data to be allocated A. Further, the terminal device may establish binding relationships between the target client data a, the target client data B, the target client data C, and the service object data B to be allocated, establish binding relationships between the target client data D, the target client data E, and the service object data a to be allocated, establish binding relationships between the target client data F and the service object data D to be allocated, and establish binding relationships between the target client data G and the service object data E to be allocated.

In the implementation, the terminal device can establish a binding relationship between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed through a preset client data distribution mode, and the client data can be rapidly distributed and reasonably distributed through the preset client data distribution mode, so that the client distribution efficiency can be improved, and the rationality of client distribution is ensured.

In some possible embodiments, after the terminal device establishes a binding relationship between each client data in the N2 target client data and one service object data to be allocated in the N1 service object data to be allocated according to the client data allocation mode, client information, client state, allocation state and service object information contained in all the client data in the client resource pool may be displayed on the terminal device. Here, the client information may include identity information included in the client data, the client state may include non-visited and visited, the allocation state may include allocated and non-allocated, and the service object information may include identity information included in the service object data. Referring to fig. 3, fig. 3 is a schematic state diagram of client data processing according to an embodiment of the present application. As shown in fig. 3, the client information of the first client includes "sheetlet, man, 158 x 632", the client status is "not visited", the allocation status is "allocated", and the service object data includes "xiao Li, man, 158 x 008". The client information of the second client includes "king, man, 139, 969", the client status is "not visited", the allocation status is "not allocated", and the service object data is "none".

Referring to fig. 4, fig. 4 is a schematic flow chart of a client data processing method according to an embodiment of the present application. It should be understood that in the present embodiment, steps S104 and S105 should be performed after step S103. As shown in fig. 4, the client data processing method specifically may include the steps of:

s104, acquiring identity information contained in any second target client data in the N2 target client data and identity information contained in second service object data to be distributed in the N1 service object data to be distributed, and determining the identity information contained in the second target client data and the identity information contained in the second service object data to be distributed as target log data.

In some possible embodiments, the terminal device may acquire identity information included in any second target client data of the N2 target client data and identity information included in second service object data to be allocated in the N1 service object data to be allocated, and determine the identity information included in the second target client data and the identity information included in the second service object data to be allocated as the target log data. And binding relation exists between the second target client data and the second service object data to be distributed.

Here, the identity information included in the second target client data may include a name, a gender, and a telephone of the second target client corresponding to the second target client data. The identity information included in the second service object data to be allocated may include a name, a sex, and a telephone of the second service object to be allocated corresponding to the second service object data to be allocated.

For example, the terminal device may obtain identity information included in any second target client data of the N2 target client data and identity information included in the second service object data to be allocated in the N1 service object data to be allocated. Here, it is assumed that the second target client data includes identity information including a name of a small sheet, a sex of a man, and a phone of 158 x 632. It is assumed that the identity information included in the second service object data to be allocated includes a name xiao Li, a sex male, and a telephone 158,008. Further, the terminal device may determine, as the target log data, identity information included in the second target client data and identity information included in the second service object data to be allocated.

In an alternative embodiment, the terminal device may acquire any second target client data of the N2 target client data and second service object data to be allocated in the N1 service object data to be allocated, and determine the second target client data and the second service object data to be allocated as the target log data. And binding relation exists between the second target client data and the second service object to be distributed.

S105, storing the target log data into a preset log database.

In some possible embodiments, the terminal device may store the target log data in a preset log database, so as to facilitate subsequent problem investigation and tracking.

In some possible embodiments, the terminal device may obtain the query condition. Further, the terminal device may determine, according to the query condition, target log data corresponding to the query condition in a preset log database. It should be noted that, the query condition includes a name of a target client corresponding to any target client data in the client data resource pool or a name of a service object to be allocated corresponding to any service object data to be allocated in the N1 service object data to be allocated.

For example, assume here that the query condition is that the target customer name is a sheetlet. The terminal device may obtain the query condition. Further, the terminal device may determine, according to the query condition, in the preset log database that the target log data corresponding to the query condition includes that the gender of the target client sheet is male, the phone is 158×632, the service object to be allocated in the binding relationship with the target client sheet Zhang Cunzai is xiao Li, the gender is male, and the phone is 158×008.

In the implementation, the terminal device determines the identity information contained in any one target client data of the N2 target client data and the identity information contained in the second service object data to be distributed in the N1 service object data to be distributed as target log data, and stores the target log data in a preset target log database. The identity information of the target client corresponding to any target client data and the service object data to be distributed which have binding relation with the target client data can be quickly and conveniently searched through the preset log database, so that the data query efficiency is improved, and the problem can be conveniently checked and tracked.

In some possible embodiments, the terminal device may perform the service information recommendation operation on the target client corresponding to each of the N2 target client data. Taking a target client corresponding to any third target client data of the N2 target client data as an example, an exemplary description will be given below of a service information recommendation operation. Specifically, the terminal device generates service recommendation information according to the third service object data to be distributed, which establishes a binding relationship according to the third target client data. Here, the service recommendation information may include identity information included in the third service object data to be allocated and/or service resources included in the third service object data to be allocated. Further, the terminal device may push service recommendation information to the target client corresponding to the third target client data.

For example, the target client corresponding to the target client data a in the N2 target client data is Zhang Wei cases, and the service object data to be distributed having a binding relationship with the target client data a is assumed to be the service object data a to be distributed. The terminal device can generate service recommendation information according to the service object data A to be distributed, which is established in a binding relation according to the target client data A, wherein the service recommendation information comprises identity information contained in the service object data A to be distributed and service resources A and service resources B contained in the service object data A to be distributed. Further, the terminal device may push the service recommendation information to the target client data a.

In the implementation, the terminal device can generate the service recommendation information according to the to-be-allocated service object data with the binding relation established by the target client data, and push the service recommendation information to the target client corresponding to the target client data, so as to help the target client to know the identity information of the to-be-allocated service object corresponding to the to-be-allocated service object data with the binding relation established by the target client in advance and the service resources owned by the to-be-allocated service object data, thereby improving the user experience.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a client data processing apparatus according to an embodiment of the present application. As shown in fig. 5, the client data processing apparatus may include: an acquisition unit 51, a processing unit 52, and a binding unit 53.

In a specific implementation, the obtaining unit 51 is configured to obtain a client data distribution mode, a client data filtering condition, and N1 pieces of service object data to be distributed, where N1 is a positive integer greater than or equal to 1. And the processing unit 52 is configured to determine N2 target client data from the client data resource pool according to the client data filtering condition, where N2 is a positive integer greater than or equal to 1. And a binding unit 53 for establishing a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the client data allocation mode.

In an alternative embodiment, the obtaining unit 51 is configured to obtain a preset screening condition of the service object data to be allocated. The processing unit 52 is configured to screen N1 service object data to be allocated from the service object resource pool according to the service object data to be allocated, where each of the N1 service object data to be allocated satisfies a service object data screening condition to be allocated.

In an alternative embodiment, the processing unit 52 is configured to determine, according to a preset first machine learning model, a first priority of each service object data to be allocated according to feature information included in each service object data to be allocated in the N1 service object data to be allocated, where the feature information included in the service object data to be allocated includes one or more of a historical success rate, a customer score, or an idle time included in the service object data to be allocated. The processing unit 52 is configured to determine, according to the first priority of each service object data to be allocated, service object data to be allocated with the highest first priority. And a binding unit 53 for establishing a binding relationship between one target client data and the service object data to be allocated having the highest first priority.

In an alternative embodiment, the processing unit 52 is configured to determine, according to the feature information included in each to-be-allocated service object data in the N1 to-be-allocated service object data, one or more of a historical success rate, a customer score, or an idle time included in each to-be-allocated service object data through a preset first machine learning model. The processing unit 52 is configured to determine, according to the feature information included in each of the N2 target client data, the second priority of each target client data through a preset second machine learning model, where the feature information included in the target client data includes one or more of a client source, a client purchase record, a client age, or a client purchase requirement included in the target client data. And a binding unit 53 for establishing a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the second priority of each target client data and the first priority of each service object data to be allocated.

In an alternative embodiment, the processing unit 52 is configured to perform the following binding operation on any first target client data of the N2 target client data, which specifically includes: the processing unit 52 is configured to determine first to-be-allocated service object data corresponding to first target client data from the N1 to-be-allocated service object data, where a second priority of the first target client data is equal to a first priority of the first to-be-allocated service object data. A binding unit 53 for establishing a binding relationship between the first target client data and the first object data to be distributed. The processing unit 52 is configured to establish a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to a result of performing the binding operation on each of the N2 target client data.

In an alternative implementation, referring to fig. 6, fig. 6 is a schematic structural diagram of a client data processing apparatus provided in the embodiment of the present application, where the apparatus may further include a storage unit 54. The processing unit 52 is configured to obtain identity information included in any second target client data of the N2 target client data and identity information included in second service object data to be allocated in the N1 service object data to be allocated, and determine the identity information included in the second target client data and the identity information included in the second service object data to be allocated as target log data, where a binding relationship exists between the second target client data and the second service object data to be allocated. And a storage unit 54, configured to store the target log data in a preset log database.

In an alternative implementation, referring to fig. 7, fig. 7 is a schematic structural diagram of a client data processing apparatus provided in the embodiment of the present application, where the apparatus may further include a pushing unit 55. The processing unit 52 is configured to execute the following service information recommendation operations on a target client corresponding to any third target client data of the N2 target client data, where the specific operation content is as follows: the processing unit 52 is configured to establish identity information included in the third service object data to be allocated and/or service resources included in the third service object data to be allocated according to the third target client data. And a pushing unit 55, configured to push service recommendation information to a target client corresponding to the third target client data.

Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may be a terminal device in the above embodiment, and may be used to implement the steps of the client data processing method performed by the terminal device described in the above embodiment. The electronic device may include: a processor 81, a memory 82 and a bus system 83.

Memory 82 includes, but is not limited to, RAM, ROM, EPROM or CD-ROM, which memory 82 is used to store related instructions and data. The memory 82 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof:

operation instructions: including various operational instructions for carrying out various operations.

Operating system: including various system programs for implementing various basic services and handling hardware-based tasks.

Only one memory is shown in fig. 8, but a plurality of memories may be provided as needed.

As shown in fig. 8, the electronic device may further include an input-output device 84, and the input-output device 84 may be a communication module or a transceiver circuit. In the embodiment of the present application, the input/output device 84 is used to perform the transceiving process of the data or signaling of the target client data, the service object data to be allocated, and the like, which are referred to in the first embodiment.

The processor 81 may be a controller, CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with the disclosure of embodiments of the present application. The processor 81 may also be a combination implementing computing functions, e.g. comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

In a specific application, the various components of the electronic device are coupled together by a bus system 83, wherein the bus system 83 may comprise a power bus, a control bus, a status signal bus, etc., in addition to a data bus. But for clarity of illustration the various buses are labeled as bus system 83 in fig. 8. For ease of illustration, only schematic illustrations are shown in fig. 8.

It should be noted that, in practical applications, the processor in the embodiments of the present application may be an integrated circuit chip, which has a signal processing capability. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a digital signal processor (digital signal Processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), an off-the-shelf programmable gate array (field programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed.

It will be appreciated that the memory in embodiments of the present application may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory. The volatile memory may be random access memory (random access memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memory described in the embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.

The embodiment of the application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a computer, implements the method or steps performed by the terminal device in the above embodiment.

The embodiment of the application also provides a computer program product, which when being executed by a computer, realizes the method or the step executed by the terminal device in the embodiment.

It should be noted that, for simplicity of description, any embodiment of the client data processing method described above is shown as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently, depending on the application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments and that the acts referred to are not necessarily required for the present application.

Although the present application has been described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the figures, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Those skilled in the art will appreciate that all or part of the steps in the various embodiments of any of the customer data processing methods described above may be accomplished by a program to instruct associated hardware, the program may be stored in a computer readable memory, the memory may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

The foregoing has described embodiments of the present application in detail, and specific examples have been used herein to illustrate the principles and implementations of a client data processing method, apparatus and related devices, the description of the foregoing embodiments being indicative of the methods and core ideas used to aid in understanding the present application; meanwhile, as those skilled in the art will appreciate, according to the concepts of a client data processing method, apparatus and related device of the present application, there are variations in the specific embodiments and application scope, and in light of the foregoing, the present disclosure should not be construed as limited to the present application.

Those of skill in the art will appreciate that in one or more of the examples described above, the functions described herein may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The foregoing embodiments have been provided for the purpose of illustrating the technical solution and advantageous effects of the present application in further detail, and it should be understood that the foregoing embodiments are merely illustrative of the present application and are not intended to limit the scope of the present application, and any modifications, equivalents, improvements, etc. made on the basis of the technical solution of the present application should be included in the scope of the present application.

Claims

1. A method of processing customer data, comprising:

acquiring a client data distribution mode, client data screening conditions and N1 pieces of service object data to be distributed, wherein N1 is a positive integer greater than or equal to 1;

n2 target client data are determined from a client data resource pool according to the client data screening conditions, wherein N2 is a positive integer greater than or equal to 1;

and establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode.

2. The method according to claim 1, wherein the obtaining N1 service object data to be allocated includes:

Acquiring preset screening conditions of service object data to be distributed;

and screening N1 pieces of service object data to be distributed from a service object resource pool according to the service object data screening conditions to be distributed, wherein each piece of service object data to be distributed in the N1 pieces of service object data to be distributed meets the service object data screening conditions to be distributed.

3. The method according to claim 1, wherein the client data allocation pattern is a priority allocation pattern, N2 is equal to 1, and the establishing a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the client data allocation pattern includes:

determining a first priority of each piece of service object data to be distributed according to characteristic information contained in each piece of service object data to be distributed in the N1 pieces of service object data to be distributed through a preset first machine learning model, wherein the characteristic information contained in the service object data to be distributed comprises one or more of historical success rate, customer score or idle time contained in the service object data to be distributed;

Determining service object data to be distributed with the highest first priority according to the first priority of each service object data to be distributed;

and establishing a binding relation between the target client data and the service object data to be distributed with the highest first priority.

4. The method according to claim 1, wherein the client data allocation mode is a priority allocation mode, N2 is greater than 1, and the establishing a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the client data allocation mode includes:

determining a second priority of each target client data according to characteristic information contained in each target client data in the N2 target client data through a preset second machine learning model, wherein the characteristic information contained in the target client data comprises one or more of a client source, a client purchase record, a client age or a client purchase requirement contained in the target client data;

And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the second priority of each target client data and the first priority of each service object data to be distributed.

5. The method of claim 4, wherein N2 is equal to N1, wherein said establishing a binding relationship between each of the N2 target client data and one of the N1 service object data to be allocated according to the second priority of each target client data and the first priority of each service object data to be allocated comprises:

executing the following binding operation on any first target client data in the N2 target client data:

determining first to-be-allocated service object data corresponding to the first target client data from the N1 to-be-allocated service object data, wherein the second priority of the first target client data is equal to the first priority of the first to-be-allocated service object data;

establishing a binding relation between the first target client data and the first service object data to be distributed;

And establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the result of executing the binding operation on each target client data in the N2 target client data.

6. The method according to any one of claims 1-5, further comprising:

acquiring identity information contained in any second target client data of the N2 target client data and identity information contained in second service object data to be distributed in the N1 service object data to be distributed, and determining the identity information contained in the second target client data and the identity information contained in the second service object data to be distributed as target log data, wherein a binding relationship exists between the second target client data and the second service object data to be distributed;

and storing the target log data into a preset log database.

7. The method according to any one of claims 1-6, wherein after said establishing a binding relationship between each of said N2 target customer data and one of said N1 service object data to be allocated according to said customer data allocation pattern, said method further comprises:

Executing the following service information recommendation operations on the target clients corresponding to any third target client data in the N2 target client data:

generating service recommendation information according to third service object data to be distributed of the third target client data in a binding relation, wherein the service recommendation information comprises identity information contained in the third service object data to be distributed and/or service resources contained in the third service object data to be distributed;

and pushing the service recommendation information to the target client corresponding to the third target client data.

8. A customer data processing apparatus, the apparatus comprising:

the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring a client data distribution mode, client data screening conditions and N1 pieces of service object data to be distributed, wherein N1 is a positive integer greater than or equal to 1;

the processing unit is used for determining N2 target client data from the client data resource pool according to the client data screening condition, wherein N2 is a positive integer greater than or equal to 1;

and the binding unit is used for establishing a binding relation between each target client data in the N2 target client data and one service object data to be distributed in the N1 service object data to be distributed according to the client data distribution mode.

9. A computer readable storage medium for storing a computer program which, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.

10. An electronic device comprising a memory storing a computer program and a processor implementing the steps of the method of any one of claims 1 to 7 when the computer program is executed.