CN111859115A

CN111859115A - User allocation method and system, data processing equipment and user allocation equipment

Info

Publication number: CN111859115A
Application number: CN202010561976.1A
Authority: CN
Inventors: 刘凯
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2020-06-18
Filing date: 2020-06-18
Publication date: 2020-10-30
Anticipated expiration: 2040-06-18

Abstract

The embodiment of the application provides a user allocation method and system, a data processing device and a user allocation device, and relates to the technical field of user allocation. The user allocation method comprises the following steps: firstly, calculating a plurality of associated information of a user to be distributed through a matching degree model to obtain the matching degree of the user to be distributed corresponding to each store; the matching degree model is obtained by training a preset model according to a plurality of pieces of associated information of the user in a historical user ordering record and a standard matching degree corresponding to an store for ordering by the user, wherein the associated information represents the association degree between the user to be distributed and one store; and secondly, distributing the users to be distributed to the corresponding stores according to the matching degree of the users to be distributed to the stores. Through the arrangement, the conversion rate of the user distribution can be improved.

Description

User allocation method and system, data processing equipment and user allocation equipment

Technical Field

The present application relates to the field of user allocation technologies, and in particular, to a user allocation method and system, a data processing device, and a user allocation device.

Background

With the development of mobile internet technology and the popularization of smart phones, the traditional car renting and selling decision path changes silently. The number of on-line car rental lines continues to increase, growing explosively in recent years.

However, the inventor researches and discovers that in the prior art, after the user corresponding to the rented and sold vehicle clue is allocated to the unmatched store, the user does not place an order at the store, and therefore the conversion rate of user allocation is low.

Disclosure of Invention

In view of the above, an object of the present application is to provide a user allocation method and system, a data processing device, and a user allocation device, so as to solve the problems in the prior art.

In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:

a user allocation method is applied to a first data processing device and comprises the following steps:

calculating a plurality of associated information of a user to be distributed through a matching degree model to obtain the matching degree of the user to be distributed corresponding to each store;

the matching degree model is obtained by training a preset model according to a plurality of pieces of associated information of the user in a historical user ordering record and a standard matching degree corresponding to an store for ordering by the user, wherein the associated information represents the association degree between the user to be distributed and one store;

And distributing the user to be distributed to the corresponding stores according to the matching degree of the user to be distributed to each store.

In a preferred selection of the embodiment of the present application, the step of assigning the user to be assigned to the corresponding store according to the matching degree of the user to be assigned to each store includes:

calculating to obtain a distribution value of the user to be distributed corresponding to each store according to the matching degree, the weight coefficient corresponding to the matching degree, the initial distribution proportion and the weight coefficient corresponding to the initial distribution proportion, wherein the initial distribution proportion represents an initial proportion of the user distributed to each store;

and allocating the user to be allocated to the corresponding store according to the allocation value.

In a preferred option of the embodiment of the present application, the user allocation method further includes:

determining an initial allocation proportion of the stores according to the ratio of the order quantity of each store to the total order quantity, wherein the order quantity represents the quantity of orders made by the user in the stores in the historical user order making record, and the total order quantity represents the sum of the order quantities of the stores in the historical user order making record.

Calculating the order processing efficiency of each store according to the order quantity of each store and the weight coefficient of the order quantity, the user satisfaction and the weight coefficient corresponding to the user satisfaction, and the order forming rate and the weight coefficient corresponding to the order forming rate;

and determining the initial distribution proportion of all the stores according to the order processing efficiency of all the stores.

In a preferred option of the embodiment of the present application, when the number of the users to be distributed is multiple, the step of distributing the users to be distributed to the corresponding stores according to the distribution value includes:

acquiring target stores of the users to be distributed according to the distribution values of the stores corresponding to the users to be distributed, wherein the target stores represent the stores corresponding to the maximum distribution values in the distribution values of the stores;

and allocating each user to be allocated to the target store.

acquiring target stores of all the users to be distributed according to the distribution values of all the stores corresponding to all the users to be distributed, wherein the target stores represent the stores corresponding to the maximum distribution values in the distribution values of all the stores;

Judging whether the target stores of each user to be distributed are the same store or not;

and if the users belong to the same store, distributing all the users to be distributed belonging to the same store to the corresponding store.

In a preferred selection of the embodiment of the present application, the association information includes a distance from the user to be allocated to each store, and the user allocation method further includes:

screening the taxi taking historical data of the users to be distributed to obtain the standing areas and the common tracks of the users to be distributed;

and determining the distance between the user to be distributed and each store according to the standing area and the common track.

In a preferred selection of the embodiment of the application, the associated information includes a consumption capability matching degree of each store corresponding to the user to be distributed, and the user distribution method further includes:

extracting and processing taxi taking historical data of the users to be allocated to obtain taxi taking grades, taxi taking frequency and residential areas of the users to be allocated;

determining the consumption capacity of the user to be allocated according to the taxi taking grade, the taxi taking frequency and the living area;

and calculating the consumption capacity matching degree of each store corresponding to the user to be distributed according to the consumption capacity of the user to be distributed and the product price of each store.

In a preferred selection of the embodiment of the present application, before the step of calculating the plurality of pieces of associated information respectively through the matching degree model to obtain the matching degrees of the to-be-allocated user corresponding to the stores, the user allocation method further includes:

training a preset model according to a plurality of associated information corresponding to the user in a historical user ordering record and a standard matching degree corresponding to the store ordered by the user to obtain the matching degree model, wherein the store ordered by the user belongs to one of the stores corresponding to the associated information.

An embodiment of the present application further provides a user allocation apparatus, including:

the matching degree calculation module is used for calculating a plurality of associated information of the user to be distributed through the matching degree model to obtain the matching degree of the user to be distributed corresponding to each store;

and the user allocation module is used for allocating the users to be allocated to the corresponding stores according to the matching degrees of the users to be allocated to the stores.

The embodiment of the present application further provides a user allocation method, which is applied to a second data processing device, and the user allocation method includes:

training a preset model according to a plurality of pieces of associated information corresponding to a user in a historical user ordering record and a standard matching degree corresponding to an store ordered by the user to obtain a matching degree model, wherein the associated information represents the associated degree between the user and one store, the store ordered by the user belongs to one of the stores corresponding to the associated information, and the matching degree model is used for calculating the associated information corresponding to each store of the user to be distributed to obtain the matching degree corresponding to each store of the user to be distributed.

An embodiment of the present application further provides a user allocation system, including:

the first data processing equipment is used for calculating a plurality of associated information of the user to be distributed through a matching degree model to obtain the matching degree of the user to be distributed corresponding to each store;

wherein the association information represents the association degree between the user to be distributed and one store;

the first data processing equipment is also used for distributing the users to be distributed to the corresponding stores according to the matching degree of the users to be distributed to the stores;

And the second data processing equipment is used for training the preset model according to a plurality of associated information corresponding to the user in the historical user ordering record and the standard matching degree corresponding to the store ordered by the user to obtain a matching degree model, wherein the store ordered by the user belongs to one of the stores corresponding to the associated information.

The embodiment of the application further provides user distribution equipment, wherein the user distribution equipment is used for training a preset model according to a plurality of pieces of associated information corresponding to the user in a historical user ordering record and the standard matching degree corresponding to the stores ordered by the user to obtain a matching degree model, the associated information represents the association degree between the user and one store, and the store ordered by the user belongs to one of the stores corresponding to the associated information;

the user distribution equipment is further used for calculating a plurality of associated information of the user to be distributed through a matching degree model to obtain the matching degree of the user to be distributed corresponding to each store;

the user distribution equipment is further used for distributing the users to be distributed to the corresponding stores according to the matching degree of the users to be distributed to the stores.

An embodiment of the present application further provides a first data processing device, which includes a memory and a processor, where the processor is configured to execute an executable computer program stored in the memory, so as to implement the steps of the user allocation method described above.

The embodiment of the application also provides a storage medium, on which a computer program is stored, and when the program is executed, the steps of the user allocation method are realized.

An embodiment of the present application further provides a second data processing device, which includes a memory and a processor, where the processor is configured to execute an executable computer program stored in the memory, so as to implement the steps of the user allocation method described above.

According to the user allocation method and system, the data processing device and the user allocation device, the multiple pieces of associated information of the user to be allocated are calculated through the matching degree model trained by the historical user ordering record, the matching degree of the user to be allocated corresponding to each store is obtained, the user to be allocated is allocated to the corresponding store according to the matching degree, the user is allocated to the matched store, and the problem that in the prior art, after the user corresponding to the rental vehicle lead is allocated to the unmatched store, the user is not ordered in the store, and therefore conversion rate of user allocation is low is solved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a block diagram of a user distribution system according to an embodiment of the present disclosure.

Fig. 2 is a block diagram of a first data processing device according to an embodiment of the present application.

Fig. 3 is a schematic flowchart of a user allocation method according to an embodiment of the present application.

Fig. 4 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 5 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 6 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 7 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 8 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 9 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 10 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Fig. 11 is another schematic flow chart of a user allocation method according to an embodiment of the present application.

Icon: 10-a user distribution system; 100-a first data processing device; 110-a network port; 120-a first processor; 130-a communication bus; 140-a first storage medium; 150-interface; 200-a second data processing device.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

In order to enable a person skilled in the art to make use of the present disclosure, the following embodiments are given. It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

Fig. 1 is a block diagram of a user allocation system 10 provided in an embodiment of the present application, which provides a possible implementation manner of the user allocation system, and referring to fig. 1, the user allocation system 10 may include a first data processing apparatus 100 and a second data processing apparatus 200.

The first data processing device 100 is configured to calculate, through the matching degree model, a plurality of pieces of associated information of the user to be distributed, obtain matching degrees of the user to be distributed corresponding to the stores, and distribute the user to be distributed to the corresponding stores according to the matching degrees of the user to be distributed corresponding to the stores. The association information characterizes the degree of association between the user to be allocated and one store.

The second data processing device 200 is configured to train a preset model according to a plurality of pieces of associated information corresponding to the user in the historical user ordering record and a standard matching degree corresponding to the store ordered by the user, so as to obtain a matching degree model, where the store ordered by the user belongs to one of the stores corresponding to the plurality of pieces of associated information.

It should be noted that, the user mentioned in this embodiment of the present application is a user who needs to rent a car, buy a car, and the store is an entity store that provides car renting and selling services, and the user allocation system provided in this embodiment of the present application may obtain the user information of the user to be allocated corresponding to the car renting and selling cue, so as to allocate the user information of the user to be allocated to the matching store, so that the user to be allocated places an order in the matching store, and the conversion rate of user allocation is improved. The user information represents identification information of the user to be allocated, and may include, for example, but not limited to, name, contact information, and the like.

Fig. 2 shows a schematic diagram of exemplary hardware and software components of a first data processing device 100, which may implement the concepts of the present disclosure, according to some embodiments of the present disclosure. The first data processing device 100 may include a network port 110 connected to a network, one or more first processors 120 for executing program instructions, a communication bus 130, and a first storage medium 140 of a different form, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the first data processing device 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present disclosure may be implemented in accordance with these program instructions. The first data processing device 100 may also comprise an Input/Output (I/O) interface 150 with other Input/Output devices, e.g. keyboard, display screen.

In some embodiments, the first processor 120 may process information and/or data related to user assignments to perform one or more of the functions described in this disclosure. In some embodiments, the first processor 120 may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, the first Processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Computing Set Computing, RISC), a microprocessor, or the like, or any combination thereof.

The first processor 120 in the first data processing device 100 may be a general purpose computer or a set purpose computer, both of which may be used to implement the user allocation method of the present disclosure. While only one computer is shown in this disclosure, for convenience, the functions described in this disclosure may be implemented in a distributed manner across multiple similar platforms to balance processing loads.

For ease of illustration, only one processor is depicted in the first data processing device 100. It should be noted, however, that the first data processing device 100 in the present disclosure may also comprise a plurality of processors, and thus the steps performed by one processor described in the present disclosure may also be performed by a plurality of processors in combination or individually. For example, if the processor of the first data processing device 100 performs step a and step B, it should be understood that step a and step B may also be performed by two different processors together or separately in one processor. For example, a first processor performs step A and a second processor performs step B, or both a first processor and a second processor perform steps A and B.

The network may be used for the exchange of information and/or data. In some embodiments, one or more components in the first data processing device 100 may send information and/or data to other components. For example, the first data processing apparatus 100 may acquire a signal via a network. Merely by way of example, the Network may include a Wireless Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a bluetooth Network, a ZigBee Network, or a Near Field Communication (NFC) Network, among others, or any combination thereof.

In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the first data processing device 100 may connect to the network to exchange data and/or information.

In some embodiments, the first data processing device 100 may comprise a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., or any combination thereof. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, control devices for smart electrical devices, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like.

With reference to fig. 3, an embodiment of the present application further provides a user allocation method, which may be applied to the first data processing apparatus 100 shown in fig. 2, where the user allocation method includes:

step S310, a plurality of associated information of the user to be distributed is calculated through the matching degree model, and the matching degree of the user to be distributed corresponding to each store is obtained.

The matching degree model is obtained by training a preset model according to a plurality of pieces of associated information of the user in the historical user ordering record and the standard matching degree corresponding to the store ordered by the user, and the associated information represents the associated degree between the user to be distributed and one store.

And step S320, distributing the users to be distributed to the corresponding stores according to the matching degrees of the users to be distributed to the stores.

According to the method, the multiple associated information of the user to be distributed is calculated through the matching degree model trained by the historical user ordering record, the matching degree of the user to be distributed corresponding to each store is obtained, the user to be distributed is distributed to the corresponding stores according to the matching degree, the user is distributed to the matched stores, and the problem that in the prior art, after the user corresponding to the renting and selling vehicle clues is distributed to the unmatched stores, the user is not in the stores for ordering, and therefore conversion rate of user distribution is low is solved.

It can be understood that the matching degree between the user to be distributed and one store can be obtained by inputting the associated information between the user to be distributed and the store into the matching degree model, and accordingly, the matching degree between the user to be distributed and a plurality of stores can be obtained by respectively inputting the associated information between the user to be distributed and the stores into the matching degree model.

It should be noted that, before step S310, the user allocation method provided in the embodiment of the present application further includes a step of training a model. Furthermore, on the basis of fig. 3, fig. 4 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, and referring to fig. 4, the user allocation method further includes:

and S330, training a preset model according to a plurality of associated information corresponding to the user in the historical user ordering record and the standard matching degree corresponding to the store ordered by the user to obtain a matching degree model.

The store ordered by the user belongs to one of the stores corresponding to the plurality of related information.

That is to say, the preset model may be trained by using a plurality of pieces of associated information corresponding to the user in the historical user ordering record as input of the preset model and using the standard matching degree corresponding to the store in which the user orders as output, so as to obtain the matching degree model.

It should be noted that the matching degree model can be implemented in various ways. For example, the matching degree model may be a deep network structure, such as a vgg (visual Geometry Group network) model, a resource (resource neural network) model, and the like. The matching degree model can support input of associated information, output of matching degree and the like.

For the associated information, it should be noted that the specific type of the associated information is not limited, and may be set according to the actual application requirements.

For example, in an alternative example, the association information includes a distance between the user to be allocated and each store, and in order to obtain the distance between the user to be allocated and each store, on the basis of fig. 3, fig. 5 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, and referring to fig. 5, the user allocation method further includes:

step S341, the taxi taking history data of the user to be allocated is screened to obtain the standing area and the common track of the user to be allocated.

And step S342, determining the distance from the user to be distributed to each store according to the standing area and the common track.

For another example, in another alternative example, the associated information includes a consumption capability matching degree of each store corresponding to the user to be allocated, and further, on the basis of fig. 3, fig. 6 is a schematic flow chart of another user allocation method provided in the embodiment of the present application, with reference to fig. 6, the user allocation method further includes:

And S343, extracting and processing the taxi taking historical data of the user to be allocated to obtain the taxi taking grade, the taxi taking frequency and the living area of the user to be allocated.

The taxi-taking grade represents the price of taxi-taking of the user, and can be divided into 10-yuan grade, 20-yuan grade, 50-yuan grade and other grades. The taxi taking frequency represents the taxi taking frequency of the user, for example, the taxi taking frequency is 30 times per month, and the taxi taking frequency can be 30 times per month.

And step S344, determining the consumption capacity of the user to be allocated according to the taxi taking grade, the taxi taking frequency and the residential area.

It should be noted that, the above three indexes may determine the consuming capability of the user to be allocated according to one or more of the three indexes, and are not limited herein.

For example, the consuming capacity of the user to be allocated may be determined only according to the rate of the living area of the user to be allocated, and it is understood that the rate of the living area of the user is high, and correspondingly, the consuming capacity of the user is strong.

When the consumption capacity of the user to be distributed is determined according to the taxi taking grade, the taxi taking frequency and the living area, the living area rate index of the user to be distributed can be determined according to the ratio of the rate of the living area of the user to be distributed to the local average rate, and the consumption capacity of the user to be distributed is obtained through calculation according to the taxi taking grade, the taxi taking frequency, the living area rate index and the corresponding weight coefficient of the user to be distributed.

And step S345, calculating the consumption capacity matching degree of each store corresponding to the user to be distributed according to the consumption capacity of the user to be distributed and the product price of each store.

It should be noted that when the consumption capability of the user to be allocated is matched with the product price of the store, the matching degree of the consumption capability of the user to be allocated corresponding to the store is high, and the matching degree of the user to be allocated corresponding to the store is obtained by calculating the multiple pieces of associated information of the user to be allocated through the matching degree model.

It is understood that the consuming capacity of the user may also be determined directly from the monthly income of the user to be allocated, without limitation. For example, the monthly income of the user to be allocated is ten thousand, the product price of the store is about one hundred thousand, and the consumption capacity of the user to be allocated can be considered to match the product price of the store.

Further, the associated information may further include historical data of the advertisement browsing of the car renting and selling of the store by the user to be allocated, such as whether to click, whether to leave contact information, the stay time and the like. The associated information may also include interaction information of the user to be allocated with the store, such as information about whether the user to be allocated is connected by the store for sale, whether the user arrives at the store for driving, and the like.

In one implementation situation, the user a to be allocated clicks the advertisement of the store a, a contact way is left under the advertisement of the store a, the stay time is long, the user a is sold and connected by the store a, the distance from the store a to the store a is the closest, the consumption capacity is matched with the store a, the plurality of associated information of the user a to be allocated are calculated through the matching degree model, and the matching degree of the store a corresponding to the user a to be allocated is the highest.

In an alternative example, the user allocation method provided in the embodiment of the present application may allocate the user to the corresponding store according to the matching degree. Specifically, the user to be assigned may be assigned to the store with the highest degree of matching.

In another alternative example, the specific situation of the store is different, for example, there is a store with low user satisfaction and/or low user ordering rate, even if the matching degree of the user to be distributed and the store is high, the user is not prompted to order after being distributed to the store, and thus the conversion rate of the user distribution is low. To avoid this situation, the embodiment of the present application further provides a possible implementation manner, specifically, on the basis of fig. 3, fig. 7 is a flowchart illustrating another user allocation method provided in the embodiment of the present application, referring to fig. 7, and step S320 includes:

Step S321, calculating according to the matching degree and the weight coefficient corresponding to the matching degree, the initial distribution proportion and the weight coefficient corresponding to the initial distribution proportion to obtain the distribution value of the user to be distributed corresponding to each store.

Wherein the initial allocation proportion characterizes an initial proportion between the allocation of the user to the respective stores. For example, each store may include store a, store B, and store C, and the initial allocation ratio may be a ratio of store a of 0.6, store B of 0.2, and store C of 0.2. That is, when the user allocation is performed, 60% of the users to be allocated are allocated to store a, 20% of the users to be allocated are allocated to store B, and 20% of the users to be allocated are allocated to store C.

It should be noted that specific values of the weight coefficient corresponding to the matching degree and the weight coefficient corresponding to the initial distribution ratio are not limited, and may be set according to actual application requirements, as long as the sum of the weight coefficient corresponding to the matching degree and the weight coefficient corresponding to the initial distribution ratio is 1. For example, in an alternative example, the matching degree may correspond to a weight coefficient of 0.5, and the initial allocation ratio may correspond to a weight coefficient of 0.5. For another example, in another alternative example, the weight coefficient corresponding to the matching degree may be 0.7, and the weight coefficient corresponding to the initial allocation ratio may be 0.3.

And step S322, distributing the users to be distributed to the corresponding stores according to the distribution values.

In detail, after the allocation values of the users to be allocated corresponding to the stores are obtained in step S321, the user to be allocated may be selected to be allocated to the store with the largest matching value. The initial distribution proportion is as follows: on the basis that the ratio of the store a is 0.6, the ratio of the store B is 0.2, and the ratio of the store C is 0.2, the matching degree between the user a to be distributed and the store a is 0.7, the matching degree between the user a to be distributed and the store B is 0.3, the matching degree between the user a to be distributed and the store C is 0.1, the weight coefficient corresponding to the matching degree may be 0.5, the weight coefficient corresponding to the initial distribution example may be 0.5, the distribution value of the store a corresponding to the user a to be distributed is 0.65, the distribution value of the store B corresponding to the user a to be distributed is 0.25, and the distribution value of the store C corresponding to the user a to be distributed is 0.15. That is, the user a to be allocated has the largest allocation value corresponding to store a, and the user a to be allocated is allocated to store a.

Alternatively, for the initial allocation ratio, it may be determined in different forms, for example:

1, determining the initial distribution proportion of each store directly according to the proportion of the order quantity; on the basis of fig. 7, fig. 8 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, and referring to fig. 8, the user allocation method further includes:

In step S351, the initial allocation ratio of the stores is determined according to the ratio of the order quantity of each store to the total order quantity.

The order quantity represents the quantity of orders of the user in stores in the historical user ordering record, and the total order quantity represents the sum of the order quantities of all stores in the historical user ordering record. For example, in the historical user order record, the number of orders placed by the user at store a is 100, the number of orders placed at store B is 200, the number of orders placed at store C is 100, the total order number is 400, and the initial allocation ratio is determined according to the ratio of the order number in the total order number: store A was at a rate of 0.25, store B was at a rate of 0.5, and store C was at a rate of 0.25.

Case 2, determining the initial distribution proportion of each store according to the order quantity, the user satisfaction degree and the order forming rate; on the basis of fig. 7, fig. 9 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, and referring to fig. 9, the user allocation method further includes:

step S352, calculating the order processing efficiency of each store according to the order quantity of each store and the weight coefficient of the order quantity, the user satisfaction and the weight coefficient corresponding to the user satisfaction, and the order forming rate and the weight coefficient corresponding to the order forming rate.

The order quantity represents the quantity of the orders of the user in the store in the historical user ordering record, the user satisfaction degree represents the satisfaction degree of the user in the store in the historical user ordering record, the order forming rate represents the ratio of the quantity of the users ordering the store in the historical user ordering record to the quantity of the users visiting the store, and the efficiency of the users visiting the store from the visit to the order is represented.

It should be noted that specific numerical values of the weight coefficient of the order quantity, the weight coefficient corresponding to the user satisfaction degree, and the weight coefficient corresponding to the order forming rate are not limited, and may be set according to actual application requirements, as long as the sum of the weight coefficient of the order quantity, the weight coefficient corresponding to the user satisfaction degree, and the weight coefficient corresponding to the order forming rate is 1. For example, in an alternative example, the weighting factor for the order quantity, the weighting factor for the user satisfaction, and the weighting factor for the order rate may be 0.2, 0.3, and 0.5, respectively.

And step S353, determining the initial allocation proportions of all stores according to the order processing efficiencies of all stores.

In detail, after the order processing efficiency of each store is obtained through step S341, the initial allocation ratio of each store may be determined according to the ratio of the order processing efficiency of each store to the sum of all the order processing efficiencies.

Further, when the number of the users to be allocated is multiple, for step S320, it may allocate the multiple users to be allocated in different manners, for example:

case 1, direct assignment of each user to a matching store; on the basis of fig. 7, fig. 10 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, referring to fig. 10, step S320 specifically includes:

step S323, acquiring target stores of the users to be distributed according to the distribution values of the stores corresponding to the users to be distributed.

The target store represents the store corresponding to the maximum distribution value in the distribution values of all stores.

In step S324, each user to be allocated is allocated to a target store.

For example, the embodiment of the application provides three users to be allocated, which are a user a to be allocated, a user B to be allocated and a user C to be allocated respectively, and three stores are a store a, a store B and a store C respectively. Determining that the corresponding store of the user A to be distributed is the store A according to the distribution values of the three stores corresponding to the user A to be distributed, and directly distributing the user A to be distributed to the store A; determining that the corresponding store of the user B to be distributed is a store A according to the distribution values of the three stores corresponding to the user B to be distributed, and directly distributing the user B to be distributed to the store A; and determining that the corresponding store of the user C to be distributed is the store C according to the distribution values of the three stores corresponding to the user C to be distributed, and directly distributing the user C to be distributed to the store C.

Case 2, determining target stores of all users, judging whether the users belong to the same store, and then distributing the users belonging to the same store; on the basis of fig. 7, fig. 11 is a schematic flowchart of another user allocation method provided in the embodiment of the present application, referring to fig. 11, and step S320 specifically includes:

step S325, acquiring target stores of all users to be allocated according to allocation values of all stores corresponding to all users to be allocated.

In step S326, it is determined whether the target store of each user to be allocated is the same store.

In the embodiment of the application, if the target stores of each user to be distributed are not the same store, each user to be distributed is distributed to the corresponding target store; if the target store of each user to be allocated is the same store, step S327 is executed.

In step S327, all users to be allocated belonging to the same store are allocated to the corresponding store.

For example, the embodiment of the application provides three users to be allocated, which are a user a to be allocated, a user B to be allocated and a user C to be allocated respectively, and three stores are a store a, a store B and a store C respectively. Determining that the corresponding store of the user A to be distributed is the store A according to the distribution values of the three stores corresponding to the user A to be distributed, determining that the corresponding store of the user B to be distributed is the store A according to the distribution values of the three stores corresponding to the user B to be distributed, and determining that the corresponding store of the user C to be distributed is the store C according to the distribution values of the three stores corresponding to the user C to be distributed. After the target stores of all the users to be distributed are obtained, the target stores of the users to be distributed A and the users to be distributed B are all stores A, the users to be distributed A and the users to be distributed B are distributed to the stores A, and the users to be distributed C are distributed to the stores C.

Further, the above step S330 of model training may be performed by the second data processing apparatus 200 alone.

That is, the present application also provides a user allocation method applied to the second data processing apparatus 200, the user allocation method including the steps of:

and training the preset model according to a plurality of associated information corresponding to the user in the historical user ordering record and the standard matching degree corresponding to the store for ordering the user to obtain a matching degree model.

The association information represents the association degree between the user and one store, the store ordered by the user belongs to one of the stores corresponding to the plurality of association information, and the matching degree model is used for calculating the plurality of association information of each store corresponding to the user to be distributed to obtain the matching degree of each store corresponding to the user to be distributed.

In some embodiments, the second data processing apparatus 200 may comprise a server. In some embodiments, the server may be a single server or a server group consisting of a plurality of servers. The set of servers can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server may be local or remote with respect to the first data processing device 100. For example, the server may access information and/or data stored in the first data processing device 100 via a network. As another example, a server may be directly connected to the first data processing device 100 to access stored information and/or data. In some embodiments, the server may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, an elastic cloud, a community cloud (communicuted), a distributed cloud, a cross-cloud (inter-cloud), a multi-cloud (multi-cloud), and the like, or any combination thereof.

A server database may be included in the server, and the server database may store data and/or instructions.

In some embodiments, the server database may store historical user ordering records. In some embodiments, the server database may store data and/or instructions for the exemplary methods described in this disclosure. In some embodiments, the server database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like.

In some embodiments, the server database may be connected to a network to communicate with one or more components in the user distribution system 10 (e.g., the first data processing device 100). One or more components in the user distribution system 10 may access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components in the user distribution system 10 (e.g., the first data processing device 100). Alternatively, in some embodiments, the database may also be part of the server.

In some embodiments, one or more components in the user assignment system 10 (e.g., the first data processing device 100) may have access to a database. In some embodiments, the first data processing device 100 may connect with a server to upload historical user order records. In other embodiments, the first data processing device 100 may be connected to a server to upload a plurality of association information of users to be distributed. In other embodiments, one or more components in the first data processing device 100 may read and/or modify information related to user assignments when certain conditions are met.

Wherein the first data processing device 100 is in communication connection with a server.

In a possible embodiment, the functions of one or more of the first data processing apparatus 100 and the second data processing apparatus 200 may be implemented by the same apparatus, for example, the embodiment of the present application may provide a user allocation apparatus, which may implement the functions of the two apparatuses.

That is to say, the embodiment of the present application further provides a user allocation device, where the user allocation device is configured to train a preset model according to a plurality of pieces of association information corresponding to a user in a historical user ordering record and a standard matching degree corresponding to an store ordered by the user, so as to obtain a matching degree model, where the association information represents an association degree between the user and one store, and the store ordered by the user belongs to one of the stores corresponding to the plurality of pieces of association information; the user distribution equipment is also used for calculating a plurality of associated information of the user to be distributed through the matching degree model to obtain the matching degree of the user to be distributed corresponding to each store; the user distribution equipment is also used for distributing the users to be distributed to the corresponding stores according to the matching degree of the users to be distributed to the stores.

Further, an embodiment of the present application provides a user matching device, where functions implemented by the user matching device correspond to steps executed by the foregoing method. The user matching device may be understood as the first data processing apparatus 100 or a processor of the first data processing apparatus 100, or may be understood as a component that is independent from the first data processing apparatus 100 or the processor and implements the functions of the present application under the control of the first data processing apparatus 100. The user matching device may include a matching degree calculation module and a user allocation module.

And the matching degree calculation module is used for calculating a plurality of associated information of the user to be distributed through the matching degree model to obtain the matching degree of the user to be distributed corresponding to each store.

The matching degree model is obtained by training a preset model according to a plurality of pieces of associated information of the user in a historical user ordering record and the standard matching degree corresponding to the store ordered by the user, wherein the associated information represents the association degree between the user to be distributed and one store.

Because the principle of the user allocation apparatus in the embodiment of the present application for solving the problem is similar to that of the user allocation method in the embodiment of the present application, the implementation of the user allocation method may refer to the implementation of the method, and repeated details are not repeated.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is executed by a processor to perform the steps of the user allocation method.

The computer program product of the user allocation method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute steps of the user allocation method in the foregoing method embodiment, which may be referred to specifically in the foregoing method embodiment, and details are not described here again.

In summary, according to the user allocation method and system, the data processing device, and the user allocation device provided in the embodiment of the present application, the multiple pieces of association information of the user to be allocated are calculated through the matching degree model trained by the historical user ordering record, so as to obtain the matching degree of the user to be allocated corresponding to each store, and the user to be allocated is allocated to the corresponding store according to the matching degree, so that the user is allocated to the matched store, and the problem that in the prior art, after the user corresponding to the rental vehicle cue is allocated to the unmatched store, the user is not ordered at the store, and thus the conversion rate of user allocation is low is solved.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A user allocation method, applied to a first data processing device, the user allocation method comprising:

2. The user allocation method according to claim 1, wherein the step of allocating the user to be allocated to the corresponding store according to the matching degree of the user to be allocated to each store comprises:

3. The user allocation method of claim 2, wherein said user allocation method further comprises:

4. The user allocation method of claim 2, wherein said user allocation method further comprises:

5. The user allocation method according to claim 2, wherein when the number of the users to be allocated is plural, the step of allocating the users to be allocated to the corresponding stores according to the allocation value includes:

and allocating each user to be allocated to the target store.

6. The user allocation method according to claim 2, wherein when the number of the users to be allocated is plural, the step of allocating the users to be allocated to the corresponding stores according to the allocation value includes:

7. The user allocation method according to claim 1, wherein the association information includes a distance of the user to be allocated from each store, the user allocation method further comprising:

8. The user allocation method according to claim 1, wherein the association information includes a consumption capability matching degree of each store corresponding to the user to be allocated, and the user allocation method further includes:

9. The user allocation method according to claim 1, wherein before the step of calculating the matching degrees of the users to be allocated to the stores by using the matching degree model to obtain the plurality of pieces of associated information, the user allocation method further comprises:

10. The user distribution equipment is characterized in that the user distribution equipment is used for training a preset model according to a plurality of associated information corresponding to a user in a historical user ordering record and a standard matching degree corresponding to stores ordered by the user to obtain a matching degree model, the associated information represents the association degree between the user and one store, and the stores ordered by the user belong to one of the stores corresponding to the associated information;