CN115907857A

CN115907857A - Order receiving reward model generation method and device, computer equipment and storage medium

Info

Publication number: CN115907857A
Application number: CN202310006522.1A
Authority: CN
Inventors: 于志杰
Original assignee: Beijing Bailong Mayun Technology Co ltd
Current assignee: Beijing Bailong Mayun Technology Co ltd
Priority date: 2023-01-04
Filing date: 2023-01-04
Publication date: 2023-04-04

Abstract

The application relates to a method for generating a receipt-receiving reward model, a method for generating a platform personalized receipt-receiving reward model, a device for generating a receipt-receiving reward model, a system for generating a platform personalized receipt-receiving reward model, a computer device and a storage medium. The method comprises the following steps: responding to the selection operation of the configuration data of the model configuration interface, and determining the attribute data of the model according to the result of the selection operation; acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model; traversing and cutting the multi-branch tree model according to the model attribute data to generate an order receiving reward model; the order receiving reward model is used for the service platform management server to count the amount of the order receiving reward of the driver according to the online car booking service data. By adopting the method, the development timeliness can be improved and the development cost can be reduced.

Description

Order receiving reward model generation method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method for generating a pick-up reward model, a method for generating a platform personalized pick-up reward model, a pick-up reward model generation device, a platform personalized pick-up reward model generation system, a computer device, and a storage medium.

Background

The network car-booking service platform is an important channel for establishing a relationship between an enterprise and a customer, and is a basic element of an enterprise business model. The order receiving reward is used as one of network car booking business management services, and the use experience of a driver user can be effectively improved, so that the positive effects of improving the loyalty of the driver customer, prolonging the life cycle of the driver customer, obtaining the improvement suggestion of a network car booking business platform and the like are generated.

However, the traditional network car booking service platform needs to perform multiple software development processes for different order receiving reward activities, and the problems of poor development timeliness or overhigh development cost and the like exist, so that the satisfaction degree of using the network car booking service platform is low.

Disclosure of Invention

In view of the above, it is necessary to provide a bill receiving reward model generation method, a platform personalized bill receiving reward model generation method, a bill receiving reward model generation device, a platform personalized bill receiving reward model generation system, a computer device, and a storage medium, which can improve development timeliness and reduce development cost.

In a first aspect, a method for generating an order-receiving reward model is provided, where the method includes:

responding to selection operation on configuration data of the model configuration interface, and determining model attribute data according to the result of the selection operation;

acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model;

traversing and cutting the multi-branch tree model according to the model attribute data to generate an order receiving reward model; the order receiving reward model is used for the service platform management server to count the order receiving reward amount of the driver according to the network car booking service data.

In one embodiment, obtaining a preset multi-way tree model includes: acquiring preset attribute data of a multi-branch tree model; and traversing and expanding the model according to the preset attribute data to obtain the multi-branch tree model.

In one embodiment, the method further includes: responding to the checking operation of the service platform of the model release interface, and counting a target platform list according to the checking operation result; the target platform list is a list of service platforms of the to-be-issued order-receiving reward model; and sending the order receiving reward model to a corresponding service platform management server according to the target platform list.

In one embodiment, the configuration data includes a pick-up reward object limit, a pick-up reward order limit, a pick-up reward duration limit, a pick-up reward calculation limit, and a pick-up reward activity visible object limit.

In one embodiment, the model attribute data comprises the node type, the number of layers of the node, the node number, the parent node number, the child node number and the child node number of each node of the order receiving reward model.

In a second aspect, a platform personalized order receiving reward model generation method is provided, and the method includes:

receiving a receipt rewarding model in any one method for generating a receipt rewarding model in the method embodiment of the first aspect, which is sent by a configuration center server;

and responding to the parameter setting operation of the order receiving reward model, and generating a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

In one embodiment, the method includes: acquiring target service data; the target service data is service data required by a platform personalized order receiving reward model; and inputting the target service data into the platform personalized order receiving reward model to obtain the driver's order receiving reward amount.

In one embodiment, the platform personalized order receiving reward model comprises a platform personalized reward object matching sub-model, a platform personalized order matching sub-model, a platform personalized duration matching sub-model and a platform personalized reward calculation sub-model; the method comprises the following steps of inputting target service data into a platform personalized order receiving reward model to obtain the sum of the order receiving reward of a driver, wherein the method comprises the following steps: inputting the target service data into a platform personalized reward object matching sub-model to obtain first service data; the first service data are service data meeting the limitation condition of the platform personalized order receiving reward object; inputting the first service data into a platform personalized order matching sub-model and a platform personalized duration matching sub-model to obtain second service data; the second service data is the service data which meets the limit condition of the platform personalized order receiving reward order and meets the limit condition of the platform personalized order receiving reward time in the first service data; and inputting the second service data into the platform personalized reward calculation submodel to obtain the order receiving reward money of the driver.

In one embodiment, the platform personalized reward calculation submodel comprises a money calculation model and a risk control model; wherein, input the second service data to the individualized reward calculation submodel of platform, obtain the driver's reward amount of receiving order, include: inputting the second service data into the amount calculation model to obtain the reward amount of the driver for issuing the receiving order; if the reward amount of the to-be-issued bill receiving of the driver is larger than or equal to the bottom-guaranteed reward amount of the driver, determining the reward amount of the to-be-issued bill receiving of the driver as the reward amount of the to-be-issued bill receiving of the driver; if the reward amount of the to-be-issued receiving order of the driver is smaller than the bottom-guaranteed reward amount of the driver, counting the total business flow amount of the driver according to the second business data, and inputting the total business flow amount of the driver into the risk control model to obtain a wind control proportion; the wind control proportion is the proportion of the total business flow sum of the driver and the bottom-guaranteed reward amount of the driver; if the wind control proportion is larger than or equal to the proportion threshold value, determining the bill receiving reward amount of the driver according to the difference between the bottom-guarantee reward amount of the driver and the bill receiving reward amount to be issued of the driver; and if the wind control proportion is smaller than the proportion threshold value, outputting a manual audit prompt notice.

In one embodiment, the platform personalized order receiving reward model comprises a platform personalized activity visible object matching sub-model; the method further comprises the following steps: inputting the target business data into a visible object matching sub-model of the platform personalized activity, and determining third business data; the third service data is the service data which accords with the visible object limiting condition of the platform personalized order receiving reward activity; and outputting a display control instruction to the corresponding user terminal according to the third service data so that the user terminal displays the propaganda information of the order receiving reward activity according to the display control instruction.

In one embodiment, acquiring target service data includes: acquiring network car booking service log data; and extracting data from the network car booking service log data to obtain target service data.

In a third aspect, an apparatus for generating a pick-up reward model is provided, the apparatus comprising: the device comprises a data determining module, a data acquiring module and a traversal clipping module.

The data determining module is used for responding to selection operation of configuration data of the model configuration interface and determining model attribute data according to a result of the selection operation; the data acquisition module is used for acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model; the traversal cutting module is used for performing traversal cutting processing on the preset multi-branch tree model according to the model attribute data to generate an order receiving reward model; the order receiving reward model is used for the service platform management server to count the amount of the order receiving reward of the driver according to the online car booking service data.

In a fourth aspect, a platform personalized order receiving reward model generation system is provided, and the system comprises a model receiving device and a model generation device.

The model receiving device is used for receiving the order-receiving reward model generated by the order-receiving reward model generating device in the third aspect device embodiment and sent by the configuration center server; the model generating device is used for responding to the parameter setting operation of the order receiving reward model, and generating the platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

In a fifth aspect, a computer device is provided, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above-mentioned method embodiments of the first and second aspects or the platform-personalized pick-up reward model generation method when executing the computer program.

In a sixth aspect, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the method for generating a pick-up reward model or the method for generating a platform-customized pick-up reward model in the above embodiments of the method of the first and second aspects.

Based on the method, the platform personalized order-receiving reward model generation method, the order-receiving reward model generation device, the platform personalized order-receiving reward model generation system, the computer equipment and the storage medium, the selection operation is carried out on the configuration data of the model configuration interface in response to the above steps, and the model attribute data is determined according to the result of the selection operation; then, acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model; and traversing and cutting the multi-branch tree model according to the model attribute data to generate an order receiving reward model for the service platform management server to count the order receiving reward amount of a driver according to the network appointment service data, so that the development of the preset multi-branch tree model can be adapted to different order receiving reward activities and the service platform management server, a traditional network appointment service platform is prevented from needing multiple software development processes for different order receiving reward activities, the development timeliness is improved, the development cost is reduced, and the satisfaction of using the network appointment service platform is improved.

Drawings

FIG. 1 is a diagram of an application environment of a method for generating a pick-up reward model and a method for generating a platform personalized pick-up reward model in one embodiment;

FIG. 2 is a first flowchart of a method for generating a pick-up reward model in one embodiment;

FIG. 3 is a flowchart illustrating steps of obtaining a predetermined multi-way tree model according to one embodiment;

FIG. 4 is a diagram illustrating a second process of a method for generating a pick-up reward model according to an embodiment;

FIG. 5 is a first flowchart of a method for generating a personalized order receiving reward model for a platform according to an embodiment;

FIG. 6 is a second flowchart of a method for generating a personalized order receiving reward model for a platform according to an embodiment;

FIG. 7 is a flowchart illustrating steps for obtaining target service data according to one embodiment;

FIG. 8 is a flowchart illustrating the steps of inputting objective business data into the platform personalized pick-up reward model to obtain the driver's pick-up reward amount in one embodiment;

FIG. 9 is a flowchart illustrating the steps of inputting the second service data into the platform personalized reward calculation submodel to obtain the driver's amount of the driver's pick-up reward in one embodiment;

FIG. 10 is a third flowchart of a method for generating a personalized order receiving reward model for a platform according to an embodiment;

FIG. 11 is a block diagram of an exemplary embodiment of an order receiving incentive model generating device;

FIG. 12 is a block diagram of the platform personalized pick-up reward model generation system in one embodiment;

fig. 13 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Embodiments of the present application are set forth in the accompanying drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, a first resistance may be referred to as a second resistance, and similarly, a second resistance may be referred to as a first resistance, without departing from the scope of the present application. The first resistance and the second resistance are both resistances, but they are not the same resistance.

It is to be understood that "connection" in the following embodiments is to be understood as "electrical connection", "communication connection", and the like if the connected circuits, modules, units, and the like have communication of electrical signals or data with each other.

As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises/comprising," "includes" or "including," etc., specify the presence of stated features, integers, steps, operations, components, parts, or combinations thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, parts, or combinations thereof.

In the following, application environments of the order taking reward model generation method and the platform personalized order taking reward model generation method provided by the embodiment of the present application will be briefly described.

As shown in fig. 1, the application environment is a bill receiving bonus statistical system, which may include a configuration center server 100 and a service platform management server 200. Wherein, the configuration center server 100 is in communication connection with the service platform management server 200.

In one particular example, the configuration center server 100 is a basic service component or device for generating a pick-up reward model. The service platform management server 200 is a component or device for performing management services on each service platform. The configuration center server 100 may be, but is not limited to being, communicatively coupled to the service platform management server 200 via a notification bus. The service platform can be but is not limited to a network car booking service platform; for example, AA trip, XX trip, BB rental or ZZ ride. The service platform manager can update the configuration data in the configuration file through the configuration center server 100, and each service platform management server 200 can obtain the order receiving reward model from the configuration center server 100, and then perform parameter setting operation on the order receiving reward model to generate a corresponding platform personalized order receiving reward model. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In a first aspect, as shown in fig. 2, a method for generating a pick-up reward model is provided, which is described by taking the method as an example of being applied to the configuration center server 100 of the pick-up reward statistical system shown in fig. 1, and the method for generating a pick-up reward model includes the following steps 202 to 206.

Step 202, responding to the selection operation of the configuration data of the model configuration interface, and determining the model attribute data according to the result of the selection operation.

It is to be appreciated that the model configuration interface is used to present configuration data; the configuration data is used for configuring the component elements of the order taking reward model and the operational logic between the component elements. In one particular example, the configuration data may also be used to configure the rendering effect and user interaction pathways of promotional information for the pick-up reward program. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein. The configuration center server 100 may determine the model attribute data according to a result of the selection operation after recognizing that the selection operation is performed on the configuration data of the model configuration interface.

In one particular example, a pick-up bonus object limiting condition refers to a condition for limiting bonus objects for pick-up bonus activities. In particular, the pick-up reward object constraints may include one or more of a pick-up reward activity city, a pick-up reward activity start time, a pick-up reward activity end time, a driver traffic type for the pick-up reward activity, a driver employment type for the pick-up reward activity, an available crowd for the pick-up reward activity, and an unavailable crowd for the pick-up reward activity. The driver service types of the order receiving reward activities comprise express cars, special cars, taxis and carpools. The driver employment types for pick-up reward activities include self-run, car-franchise, and no-car franchise. The participatable crowd in the order-receiving reward activity refers to target drivers which can participate in the order-receiving reward activity and are selected based on the personalized rule; for example, elite drivers who finished their cars more than 30 times a day in the last week, or silent drivers who did not leave the car in the last three months. The non-participatory crowd of the order-receiving reward activity refers to target drivers who are circled based on the personalized rules and cannot participate in the order-receiving reward activity. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In one particular example, the pick-up reward order limiting condition refers to a condition for limiting a service order for a pick-up reward campaign. Specifically, the pick-up reward order limiting condition may include one or more of a pick-up reward activity city, a pick-up reward activity start time, a pick-up reward activity end time, an order business type of the pick-up reward activity, an order period requirement of the pick-up reward activity, an order area requirement of the pick-up reward activity, a tally order requirement of the pick-up reward activity, a no-pay order requirement of the pick-up reward activity, an order timeliness type of the pick-up reward activity, an order mileage range of the pick-up reward activity, and an order pipelining range of the pick-up reward activity. The order business types of the order receiving reward activities comprise unlimited types, express orders and special orders. Order time requirements for pick-up reward activities include one or more of morning rush, noon, evening rush and night. Order area requirements of the order receiving reward activities comprise designated administrative areas and electronic fences; for example, the designated administrative area may be, but is not limited to, the Haishen area of Beijing, and the electronic fence may be, but is not limited to, within five rings of Beijing. The prize-counting order requirements of the order-receiving reward activities comprise unlimited types, special price orders and carpooling orders; the non-lottery order requirements of the order receiving reward activities comprise unlimited types, special price orders, car sharing orders and road ordering orders; the order aging types of the order receiving reward activities comprise unlimited types, real-time orders and reservation orders; the order mileage range of the order receiving reward activity comprises a preset mileage interval; for example, the preset mileage interval may be, but is not limited to, 3 to 30km; the order flow range of the order receiving reward activity comprises a preset flow range; for example, the preset order pipelining range may be, but is not limited to, 0 to 30-yuan. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In one particular example, the pickup award duration limiting condition refers to a condition for limiting an activity participation duration of the pickup award activity. Specifically, the order taking reward duration limiting condition comprises one or more of an order taking reward activity city, an order taking reward activity starting time, an order taking reward activity ending time, an order listening mode of the order taking reward activity, an order period requirement of the order taking reward activity and an order area requirement of the order taking reward activity. The order listening mode of the order receiving reward activity comprises unlimited types, the total duration is not accounted by only listening to the reservation order time, and the total duration is not accounted by only listening to the assignment order time. Order time requirements for the pick-up incentive campaign include one or more of morning rush hour, noon, evening rush hour, and night. Order area requirements of the order receiving reward activities comprise designated administrative areas and electronic fences; for example, the designated administrative area may be, but is not limited to, the Haishen area of Beijing, and the electronic fence may be, but is not limited to, within five rings of Beijing. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In one specific example, the order receiving reward calculation limiting condition comprises an order receiving reward activity starting time, an order receiving reward activity ending time, a task period of the order receiving reward activity, a reward settlement period of the order receiving reward activity and a task index of the order receiving reward activity. Wherein, the task period of the order receiving reward activity comprises a daily task and a periodic task. For example, a daily task may be, but is not limited to, recalculating task completion progress for the day from zero point of day, and a periodic task may be, but is not limited to, an indicator of understanding overall task completion progress within an activity validity period, progress of the day on a previous day added to an amount or duration of the completion of the individual. The reward settlement period of the order receiving reward activity comprises daily awards and periodic awards; for example, a daily award may be, but is not limited to, a reward that is issued daily based on the completion of the mission on the previous day, and a periodic award may be, but is not limited to, a uniform award that is issued to all eligible drivers after the end of the event. The above are only specific examples, and the setting is flexible in practical application according to user requirements, and is not limited here.

In one particular example, the task metrics for the pick-up reward activity in the pick-up reward calculation constraints may include one or more of an end-of-order task requirement, a departure duration task requirement, an order post-commission flow requirement, a rights and interests mode requirement, a reward upper limit requirement, a per-unit interval reward requirement, and a risk control requirement.

Specifically, the completion amount task requires the inclusion of a completion amount indicator. The order completion amount index refers to the total number of orders that meet the order taking reward object limit and the order taking reward order limit. In addition, the order quantity completion task requirement can also comprise a time period limit of the order quantity completion index; that is, a restriction period may be added for the departure duration index statistics; for example, a web appointment platform may provide targeted rewards to drivers who have contributions to their departure during morning and evening rush hours.

Specifically, the departure time task requirement includes a departure time index. The departure time index refers to the total time length meeting the limit condition of the order receiving reward object and the limit condition of the order receiving reward time length. In addition, the departure time task requirement can also comprise time period limit of the departure time index; that is, a restriction period may be added for the departure duration index statistics; for example, a web appointment platform may provide targeted rewards to drivers who have contributions to their departure during morning and evening rush hours.

In particular, the running water requirement after order drawing is mainly applicable to a scenario with a guaranteed-base reward requirement. The flowing water after the order is extracted refers to the rest part after the driver finishes the order and the network car appointment business platform extracts part of the order. The running requirement after the order is extracted is set, so that a driver can intentionally receive only small short distance orders to quickly collect large amount of rewards; the driver who can only receive small orders at all times can be rewarded and compensated, and the driver can be saved to continuously provide the transport capacity on the platform.

Specifically, the equity mode requirements include one or more of cash, platform credits, fueling coupons, charging coupons, car washes, and commission exemptions. The reward upper limit requirement may be, but is not limited to, a maximum of 30 dollars for a single day, a maximum of 20 dollars for a single day, or a 2 dollar upper limit for the amount of money awarded per unit, etc. The reward requirement of each single interval can be set in a gradient way; for example, each of the 1 st through 10 th orders awards 0.5 yuan, each of the 11 th through 20 th orders awards 1 yuan, and each of the 21 st through 35 th orders awards 1.5 yuan.

Specifically, the risk control requirements may include a cursory wind control index and a baseload wind control index. The brushing single wind control index can be but is not limited to a brushing single wind control coefficient; the billing wind control coefficient is the proportion of the sum of the awards of the to-be-issued receiving bills of the driver and the total business running sum of the driver; that is, when the billing wind control coefficient is smaller than the billing wind control coefficient proportional threshold, a manual review prompt notification needs to be sent in time to prompt that the award can be issued only after the manual review is passed; the bottom-protecting wind control index can be but is not limited to a bottom-protecting wind control coefficient; the bottom-preserving wind control coefficient is the proportion of the total business running amount of the driver and the bottom-preserving reward amount of the driver; that is to say, when the base protection wind control coefficient is smaller than the base protection coefficient proportional threshold, a prompt for manual review needs to be sent in time to prompt that the prize can be awarded after the manual review is passed. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In one particular example, the pickup reward activity visible object restriction condition refers to a condition for restricting visible objects of a pickup reward activity. In particular, the pick-up reward activity visible object constraints may include one or more of a pick-up reward activity city, a pick-up reward activity start time, a pick-up reward activity end time, a pick-up reward activity driver business type, a pick-up reward activity driver employment type, a pick-up reward activity attendeable crowd, and a pick-up reward activity non-attendeable crowd. The driver service types of the order receiving reward activities comprise express cars, special cars and taxis. The driver employment types for pick-up reward activities include self-run, car-on affiliate, and car-off affiliate. The participatable crowd in the order-receiving reward activity refers to target drivers which can participate in the order-receiving reward activity and are selected based on the personalized rule; for example, elite drivers who finished their cars more than 30 times a day in the last week, or silent drivers who did not leave the car in the last three months. The non-participatable crowd in the order-receiving reward activity refers to target drivers who cannot participate in the order-receiving reward activity and are selected based on personalized rules; for example, drivers who received complaints more than 50 times in a cumulative manner over the last three months. Further, it will be appreciated that the number of drivers eligible for the pickup reward program visual object limit is greater than the number of drivers eligible for the pickup reward program object limit. The above are only specific examples, and the setting is flexible in practical application according to user requirements, and is not limited here.

And step 204, acquiring a preset multi-branch tree model.

The multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model.

Specifically, the preset rewarding object matching sub-model is used for judging drivers meeting preset order receiving rewarding object limiting conditions; the preset order matching sub-model is used for judging orders meeting the preset order receiving reward order limiting conditions; the preset duration matching sub-model is used for judging orders meeting the preset order receiving reward duration limiting condition; the preset reward calculation sub-model is used for calculating the order receiving reward amount of the order meeting the preset order receiving reward calculation limiting conditions; the preset activity visible object matching sub-model is used for judging the drivers meeting the preset limitation condition of the order receiving reward activity visible object.

In one embodiment, as shown in fig. 3, a preset multi-way tree model is obtained, which includes

steps

301 and 302.

Step 301, obtaining preset attribute data of the multi-branch tree model.

And 302, performing model traversal expansion according to preset attribute data to obtain a multi-branch tree model.

The configuration center server 100 may obtain preset attribute data of a preset multi-branch tree model; and then, performing model traversal expansion according to the preset attribute data to obtain a preset multi-branch tree model.

It is understood that the preset attribute data includes a node type, a number of layers of nodes, a node number, a parent node number, a number of child nodes, and a child node number of each node of the preset multi-branch tree model. In addition, compared with any order receiving reward model, the number of the nodes, the node types and the number of the layers where the nodes are located of the preset multi-branch tree model is the largest and the largest.

In this embodiment, in the process of acquiring a preset multi-branch tree model, preset attribute data of the multi-branch tree model is acquired first; then, model traversal expansion is carried out according to the preset attribute data to obtain the multi-branch tree model, and convenience and efficiency of obtaining the preset multi-branch tree model are improved.

And step 206, traversing and cutting the multi-branch tree model according to the model attribute data to generate an order receiving reward model.

The order receiving reward model is used for the service platform management server 200 to count the amount of the order receiving reward of the driver according to the online car booking service data. The configuration center server 100 may perform traversal clipping processing on a preset multi-branch tree model according to the model attribute data, thereby generating an order receiving reward model.

In one embodiment, the model attribute data comprises the node type, the number of layers of the node, the node number, the parent node number, the number of child nodes and the child node number of each node of the order receiving reward model.

In a specific example, compared with any order receiving reward model, the number of nodes, node types and the number of layers where the nodes are located of the preset multi-branch tree model is the largest and the largest; the configuration center server 100 may perform traversal clipping processing on each node in the preset multi-branch tree model according to model attribute data including the node type, the number of layers where the node is located, the node number, the parent node number, the child node number, and the child node number of each node of the order receiving reward model based on a preset traversal order, and may generate the order receiving reward model. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

In one embodiment, the order receiving reward model comprises a reward object matching sub-model, an order matching sub-model, a duration matching sub-model, a reward calculation sub-model and an activity visible object matching sub-model.

Specifically, the reward object matching sub-model is used for judging drivers meeting the limitation condition of the order-receiving reward object; the order matching sub-model is used for judging orders meeting the order receiving reward order limiting conditions; the time length matching sub-model is used for judging orders meeting the time length limiting conditions of order receiving rewards; the reward calculation sub-model is used for calculating the order receiving reward amount of the order meeting the order receiving reward calculation limiting conditions; the activity visible object matching sub-model is used for judging drivers meeting the limitation condition of the activity visible object of the order receiving reward.

Based on the above, the order receiving reward model generation method responds to the selection operation of the configuration data of the model configuration interface, and determines the model attribute data according to the result of the selection operation; then, acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model; and traversing and cutting the multi-branch tree model according to the model attribute data to generate an order receiving reward model for the service platform management server to count the order receiving reward amount of a driver according to the network appointment service data, so that the development of the preset multi-branch tree model can be adapted to different order receiving reward activities and the service platform management server, a traditional network appointment service platform is prevented from needing multiple software development processes for different order receiving reward activities, the development timeliness is improved, the development cost is reduced, and the satisfaction of using the network appointment service platform is improved.

In one embodiment, as shown in fig. 4, the method further includes

steps

401 and 402.

Step 401, in response to the operation of checking the service platform of the model issuing interface, counting a target platform list according to the result of the checking operation;

and step 402, sending the order receiving reward model to a corresponding service platform management server according to the target platform list.

The target platform list is a list of service platforms of the order receiving reward model to be issued. It can be understood that the target platform list at least comprises a service platform of the reward model to be issued for receiving orders; each service platform is bound on the corresponding service platform management service.

Specifically, the configuration center server 100 may count a target platform list according to a result of the checking operation when the checking operation is identified for the service platform of the model publishing interface; and then, according to the target platform list, the order receiving reward model is sent to the corresponding service platform management server, so that the service platform management server receives the order receiving reward model, can respond to the parameter setting operation of the order receiving reward model, and generates a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

In one specific example, the configuration center server 100 displays a model publishing interface, and the service platforms displayed on the model publishing interface include AA travel, XX travel, BB rental, and ZZ riding. A manager configuring the central server 100 may perform a selection operation on an AA trip, an XX trip, a BB rental, and a ZZ ride displayed on the model release interface, where the selection operation specifically selects the AA trip and the XX trip; the configuration center server 100 may count a target platform list according to a result of the checking operation when the checking operation of the service platform of the model publishing interface is recognized; at this time, the list of the target platform comprises an AA trip and an XX trip; and then, according to the AA trip and the XX trip included in the target platform list, sending the order receiving reward model to the service platform management servers corresponding to the AA trip and the XX trip respectively. The above are only specific examples, and the setting is flexible in practical application according to user requirements, and is not limited here.

In this embodiment, in response to a colluding operation performed on a service platform of a model publishing interface, a target platform list is counted according to a result of the colluding operation; then, the order receiving reward model is sent to the corresponding service platform management server 200 according to the target platform list, so that the service platform management server 200 can conveniently perform personalized setting on the received order receiving reward model, and the convenience of the order receiving reward model generation method is improved.

In a second aspect, as shown in fig. 5, a method for generating a platform personalized pick-up reward model is provided, which is described by taking the method as an example of applying the method to the service platform management server 200 of the pick-up reward statistical system shown in fig. 1, and the method includes steps 502 to 504.

Step 502, receiving a receipt reward model in any one receipt reward model generation method in the method embodiment of the first aspect sent by a configuration center server;

and 504, responding to the parameter setting operation of the order receiving reward model, and generating a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

The service platform management server 200 may receive the order receiving reward model in the method for generating an order receiving reward model in any one of the first aspect method embodiments sent by the configuration center server 100; and then, when the parameter setting operation of the order receiving reward model is identified, generating a platform personalized order receiving reward model according to the result of the parameter setting operation of the order receiving reward model and the order receiving reward model.

In a specific example, the parameter setting operation of the ticket reward model refers to a setting operation of an item configuration value corresponding to configuration data of the ticket reward model. The configuration data of the order receiving reward model comprises an order receiving reward object limiting condition, an order receiving reward order limiting condition, an order receiving reward duration limiting condition, an order receiving reward calculation limiting condition and an order receiving reward activity visible object limiting condition. For example, the pick-up reward object limit may include a pick-up reward activity city. The manager of the service platform management server 200 can set the configuration value of the bill receiving reward activity city in the configuration data of the bill receiving reward model through the service platform management server 200, and set the configuration value of the bill receiving reward activity city to Beijing and Changsha through the setting operation, so that the platform personalized bill receiving reward model is generated according to the result of the setting operation, and the personalized bill receiving reward activity that the bill receiving reward object is only Beijing and Changsha can be issued through the platform personalized bill receiving reward model. The above is only a specific example, and the actual application is flexibly set according to the user requirement, and is not limited herein.

Based on this, the platform personalized order receiving reward model generation method receives the order receiving reward model in any order receiving reward model generation method in the first aspect method embodiment sent by the configuration center server; and then, responding to the parameter setting operation of the order receiving reward model, and generating a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model, so that each network appointment business platform can generate the platform personalized order receiving reward model meeting personalized requirements through the parameter setting operation of the order receiving reward model, multiple software development processes of the traditional network appointment business platform aiming at different order receiving reward activities are avoided, the development timeliness and convenience are improved, the development cost is reduced, and the satisfaction degree of using the network appointment business platform is improved.

In one embodiment, as shown in fig. 6, the method includes

steps

601 and 602.

Step 601, obtaining target service data.

The target service data is service data required by the platform personalized order receiving reward model. The service platform management server 200 acquires target service data.

In one embodiment, as shown in fig. 7, acquiring target service data includes

steps

701 and 702.

And step 701, acquiring network appointment service log data.

And step 702, extracting data of the network car booking service log data to obtain target service data.

The service platform management server 200 may obtain the log data of the network car booking service; and then, data extraction is carried out on the acquired online car booking service log data, so that target service data are obtained, and calculation of the driver order receiving reward amount is facilitated.

In a specific example, the step of extracting data from the network car booking service log data to obtain target service data includes: storing the network car booking service log data into a message queue; calling a streaming calculation engine, and reading network car booking service log data from the message queue; and writing the read network appointment service log data into a text analysis engine to obtain target service data. Further, the message queue may be, but is not limited to, an asynchronous message queue. The above is only a specific example, and the setting is flexible according to the user requirement in practical application, and is not limited herein.

In the embodiment, the log data of the network car booking service is acquired; and then, data extraction is carried out on the network appointment service log data to obtain target service data, so that calculation of the driver's bill receiving reward amount is facilitated, and convenience in obtaining the target service data is improved.

Step 602, inputting the target service data into the platform personalized order receiving reward model to obtain the driver's order receiving reward amount.

The service platform management server 200 inputs the acquired target service data into the platform personalized order receiving reward model, and the amount of the driver's order receiving reward can be obtained.

In the embodiment, target service data is acquired; the target service data is service data required by a platform personalized order receiving reward model; and then, target service data is input into the platform personalized order receiving reward model to obtain the reward amount of the order receiving of the driver, so that the service platform management server 200 can conveniently distribute rewards according to the reward amount of the order receiving reward of the driver, and the convenience and the efficiency of calculating the reward amount of the order receiving of the driver are improved.

In one embodiment, as shown in fig. 8, the platform personalized pick-up reward model includes a platform personalized reward object matching sub-model, a platform personalized order matching sub-model, a platform personalized duration matching sub-model and a platform personalized reward calculation sub-model.

The target service data is input into the platform personalized order receiving reward model to obtain the driver's order receiving reward amount, and the steps 801 to 803 are included.

Step 801, inputting the target service data into the platform personalized reward object matching sub-model to obtain first service data.

Step 802, inputting the first service data into a platform personalized order matching submodel and a platform personalized duration matching submodel to obtain second service data.

And 803, inputting the second service data into the platform personalized reward calculation submodel to obtain the driver's bill receiving reward amount.

The first service data are service data which accord with the limit conditions of the platform personalized order receiving reward objects; the second service data is the service data which accords with the limit condition of the platform personalized order receiving reward order and the limit condition of the platform personalized order receiving reward duration in the first service data.

Specifically, the service platform management server 200 inputs the acquired target service data to the platform personalized reward object matching sub-model to obtain first service data; then, inputting the first service data into a platform personalized order matching submodel and a platform personalized duration matching submodel to obtain second service data; and then, inputting the second service data into the platform personalized reward calculation submodel to obtain the driver order receiving reward amount, thereby realizing calculation of the driver order receiving reward amount corresponding to the order which simultaneously meets the platform personalized order receiving reward object limit condition, the platform personalized order receiving reward order limit condition and meets the platform personalized order receiving reward time limit condition according to the target service data.

In the embodiment, the target service data is input into the platform personalized reward object matching sub-model to obtain first service data; inputting the first service data into a platform personalized order matching sub-model and a platform personalized duration matching sub-model to obtain second service data; and inputting the second service data into the platform personalized reward calculation submodel to obtain the driver order receiving reward amount, thereby accurately and quickly calculating the driver order receiving reward amount and improving the convenience and efficiency of the order receiving reward amount.

In one embodiment, as shown in fig. 9, the platform personalized reward calculation submodel includes an amount calculation model and a risk control model.

And inputting the second service data into the platform personalized reward calculation submodel to obtain the driver order receiving reward amount, wherein the steps from 901 to 905 are included.

And step 901, inputting the second service data into the amount calculation model to obtain the reward amount of the driver for sending the receiving order.

And step 902, determining the reward amount of the to-be-issued bill as the reward amount of the driver's bill if the reward amount of the to-be-issued bill is larger than or equal to the bottom-guaranteed reward amount of the driver.

And 903, counting the total business flow amount of the driver according to the second business data if the to-be-issued receiving list reward amount of the driver is smaller than the bottom-guaranteed reward amount of the driver, and inputting the total business flow amount of the driver to the risk control model to obtain the wind control proportion.

And 904, if the wind control proportion is larger than or equal to the proportion threshold, determining the bill receiving reward amount of the driver according to the difference between the bottom-guaranteed reward amount of the driver and the to-be-issued bill receiving reward amount of the driver.

And step 905, outputting a manual review prompt notice if the wind control ratio is smaller than the ratio threshold.

The wind control proportion is the proportion of the total business flow sum of the driver and the bottom-guaranteed reward sum of the driver. The service platform management server 200 inputs the second service data into the amount calculation model to obtain the reward amount of the driver to be issued and received list; that is, the service platform management server 200 inputs the service data meeting the platform personalized pick-up reward order limit condition and meeting the platform personalized pick-up reward duration limit condition in the first service data into the amount calculation model, so as to obtain the pick-up reward amount theoretically required by the network appointment service platform for the driver, that is, the reward amount of the pick-up reward to be issued; then, when the reward amount of the receiving order to be issued of the driver is larger than or equal to the bottom-protection reward amount of the driver, the income of the driver is better without performing bottom-protection reward, and the reward amount of the receiving order to be issued of the driver can be directly determined as the reward amount of the receiving order of the driver; meanwhile, when the reward amount of the to-be-issued receiving order of the driver is smaller than the bottom-guaranteed reward amount of the driver, and the income of the driver is poor at the moment, the bottom-guaranteed reward is needed, the total business flow amount of the driver is counted according to the second business data, and the total business flow amount of the driver is input into the risk control model so as to obtain the wind control proportion; then, when the wind control proportion is larger than or equal to the proportion threshold, the driver is not at risk of collecting the guarantee base reward amount, namely the driver order receiving reward amount can be determined according to the difference between the guarantee base reward amount of the driver and the driver order receiving reward amount to be issued, so that the order receiving reward amount is issued to the corresponding driver; and when the wind control proportion is smaller than the proportion threshold, the driver is indicated to have the risk of collecting the bottom-guaranteed reward amount, and then a manual audit prompt notice can be output, so that an administrator of the service platform management server can audit the condition in time, and the loss of the reward amount is avoided.

In this embodiment, through the above steps 901 to 905, not only can the driver with poor income be awarded with the end-protection prize, so as to improve the loyalty of the driver customer and prolong the life cycle of the driver customer, but also the risk control is performed on the end-protection prize, so as to reduce the operation cost of the network car booking service platform.

In one embodiment, as shown in fig. 10, the platform personalized pick-up reward model comprises a platform personalized activity visible object matching sub-model; the method further comprises step 1001 and step 1002.

And 1001, inputting the target service data into the platform personalized activity visible object matching sub-model, and determining third service data.

And step 1002, outputting a display control instruction to the corresponding user terminal according to the third service data, so that the user terminal displays the propaganda information of the order-receiving reward activity according to the display control instruction.

And the third service data is the service data which accords with the visible object limitation condition of the platform personalized order receiving reward activity. It can be understood that each driver logging in the network car booking service platform is provided with a corresponding user terminal, and each user terminal is in communication connection with the service platform management server, so that each driver can know information issued by the network car booking service platform through the corresponding user terminal. Specifically, the user terminal may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, which are only specific examples, and the user terminal may be flexibly configured according to user requirements in practical applications, and is not limited herein.

Specifically, the service platform management server 200 inputs the target service data to the platform personalized campaign visible object matching sub-model, that is, the service data meeting the platform personalized order receiving reward campaign visible object restriction condition, that is, the third service data, can be determined; and then, outputting a display control instruction to the corresponding user terminal according to the third service data so that the user terminal displays the propaganda information of the order-receiving reward activity according to the display control instruction, thereby improving the convenience and the efficiency of issuing the propaganda information of the order-receiving reward activity and also improving the convenience and the efficiency of knowing the propaganda information of the order-receiving reward activity by a driver.

In the embodiment, the third business data is determined by inputting the target business data into the platform personalized activity visible object matching sub-model; and then, outputting a display control instruction to the corresponding user terminal according to the third service data so that the user terminal displays the propaganda information of the order-receiving reward activity according to the display control instruction, thereby improving the convenience and the efficiency of issuing the propaganda information of the order-receiving reward activity and also improving the convenience and the efficiency of knowing the propaganda information of the order-receiving reward activity by a driver.

It should be understood that although the various steps in the flow charts of fig. 2-10 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-10 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the sub-steps or stages of other steps.

In a third aspect, as shown in fig. 11, an apparatus for generating a pick-up reward model is provided, and the apparatus includes a data determining module 1101, a data obtaining module 1102 and a traversal clipping module 1103.

The data determination module 1101 is configured to, in response to a selection operation performed on configuration data of the model configuration interface, determine model attribute data according to a result of the selection operation; the data obtaining module 1102 is configured to obtain a preset multi-way tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model; the traversal clipping module 1103 is configured to perform traversal clipping processing on the preset multi-branch tree model according to the model attribute data, and generate an order-receiving reward model; the order receiving reward model is used for the service platform management server to count the order receiving reward amount of the driver according to the network car booking service data.

In one embodiment, the data acquisition module 1102 includes a data acquisition unit and a model traversal extension unit.

The data acquisition unit is used for acquiring preset attribute data of the multi-branch tree model; and the model traversal extension unit is used for performing model traversal extension according to preset attribute data to obtain a multi-branch tree model.

In one embodiment, the apparatus further includes a list confirmation module and a model transmission module.

The list confirmation module is used for responding to the collusion operation of the service platform of the model release interface and counting a target platform list according to the result of the collusion operation; the target platform list is a list of service platforms of the order receiving and rewarding model to be issued; and the model sending module is used for sending the order receiving reward model to the corresponding service platform management server according to the target platform list.

For specific limitations of the order taking reward model generation device, reference may be made to the above limitations on the order taking reward model generation method, and details are not repeated here. The modules in the order reward model generation device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In a fourth aspect, as shown in fig. 12, a platform personalized pick-up reward model generation system is provided, which includes a model receiving device 1201 and a model generating device 1202.

The model receiving device 1201 is configured to receive the order-receiving reward model generated by the order-receiving reward model generating device in the apparatus embodiment of the third aspect and sent by the configuration center server; the model generating device 1202 is configured to generate a platform personalized order receiving reward model according to a result of the parameter setting operation and the order receiving reward model in response to the parameter setting operation of the order receiving reward model.

In one embodiment, the system comprises a service data acquisition device and an order receiving reward amount calculation device.

The service data acquisition device is used for acquiring target service data; the target service data is service data required by a platform personalized order receiving reward model; and the order receiving reward amount calculation device is used for inputting the target service data into the platform personalized order receiving reward model to obtain the order receiving reward amount of the driver.

In one embodiment, the platform personalized order receiving reward model comprises a platform personalized reward object matching sub-model, a platform personalized order matching sub-model, a platform personalized duration matching sub-model and a platform personalized reward calculation sub-model; the order receiving reward amount calculation device comprises a first service data determination unit, a second service data determination unit and an order receiving reward amount calculation unit.

The first service data determining unit is used for inputting the target service data into the platform personalized reward object matching sub-model to obtain first service data; the first service data is service data which accords with the limit condition of the platform personalized order receiving reward object; the second service data determining unit is used for inputting the first service data into the platform personalized order matching sub-model and the platform personalized duration matching sub-model to obtain second service data; the second service data is the service data which accords with the limit condition of the platform personalized order receiving reward order and the limit condition of the platform personalized order receiving reward duration in the first service data; and the order receiving reward amount calculation unit is used for inputting the second service data into the platform personalized reward calculation submodel to obtain the order receiving reward amount of the driver.

In one embodiment, the platform personalized reward calculation submodel comprises a money calculation model and a risk control model; wherein, the receiving reward amount calculation unit comprises a receiving reward amount calculation subunit.

The bill receiving and rewarding amount calculation subunit is used for inputting the second service data into the amount calculation model to obtain the rewarding amount of the driver for sending the bill receiving and rewarding; the bill receiving reward amount calculating subunit is used for determining the reward amount of the bill to be issued of the driver as the bill receiving reward amount of the driver if the reward amount of the bill to be issued of the driver is larger than or equal to the bottom-guaranteed reward amount of the driver; the order receiving reward amount calculation subunit is used for counting the service running total amount of the driver according to the second service data if the to-be-issued order receiving reward amount of the driver is smaller than the bottom-guaranteed reward amount of the driver, and inputting the service running total amount of the driver to the risk control model to obtain a wind control proportion; the wind control proportion is the proportion of the total business flow sum of the driver and the bottom-guaranteed reward amount of the driver; the bill receiving reward amount calculation subunit is used for determining the bill receiving reward amount of the driver according to the difference between the bottom-protected reward amount of the driver and the to-be-issued bill receiving reward amount of the driver if the wind control proportion is larger than or equal to the proportion threshold value; and the order receiving reward amount calculation subunit is used for outputting a manual review prompt notice if the wind control proportion is smaller than the proportion threshold value.

In one embodiment, the platform personalized order receiving reward model comprises a platform personalized activity visible object matching sub-model; the system also comprises a third service data determining unit and a control instruction output unit.

The third service data determining unit is used for inputting the target service data into the platform personalized activity visible object matching sub-model and determining the third service data; the third service data is the service data which accords with the visible object limitation condition of the platform personalized order receiving reward activity; and the control instruction output unit is used for outputting a display control instruction to the corresponding user terminal according to the third service data so that the user terminal can display the propaganda information of the order receiving reward activity according to the display control instruction.

For specific limitations of the platform personalized pick-up reward model generation system, reference may be made to the above limitations on the platform personalized pick-up reward model generation method, and details are not repeated here. The modules in the platform personalized pick-up reward model generation system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 13. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the order receiving reward model and the platform personalized order receiving reward model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a receiving reward model generation method or a platform personalized receiving reward model generation method.

Those skilled in the art will appreciate that the architecture shown in fig. 13 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A pick-up reward model generation method, the method comprising:

responding to selection operation of configuration data of a model configuration interface, and determining model attribute data according to a result of the selection operation;

traversing and cutting the multi-branch tree model according to the model attribute data to generate an order-receiving reward model; and the order receiving reward model is used for the service platform management server to count the order receiving reward amount of the driver according to the online car booking service data.

2. The method of claim 1, wherein the obtaining the preset multi-way tree model comprises:

acquiring preset attribute data of the multi-branch tree model;

and performing model traversal expansion according to the preset attribute data to obtain the multi-branch tree model.

3. The method of claim 1, further comprising:

responding to the checking operation of a service platform of a model release interface, and counting a target platform list according to the checking operation result; the target platform list is a list of service platforms to be issued with the order receiving reward model;

and sending the order receiving reward model to the corresponding service platform management server according to the target platform list.

4. The method of claim 1, wherein the configuration data includes a pick-up reward object limit, a pick-up reward order limit, a pick-up reward duration limit, a pick-up reward calculation limit, and a pick-up reward activity visible object limit.

5. The method of claim 1, wherein the model attribute data comprises a node type, a number of layers in which the node is located, a node number, a parent node number, a number of child nodes, and a number of child nodes of each node of the pick-up reward model.

6. A platform personalized order receiving reward model generation method is characterized by comprising the following steps:

receiving the order receiving reward model in the order receiving reward model generating method of any one of claims 1 to 5 sent by the configuration center server;

responding to the parameter setting operation of the order receiving reward model, and generating a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

7. The method of claim 6, further comprising:

acquiring target service data; the target service data is service data required by using the platform personalized order receiving reward model;

and inputting the target service data into the platform personalized order receiving reward model to obtain the driver's order receiving reward amount.

8. The method of claim 7, wherein the platform personalized order receiving reward model comprises a platform personalized reward object matching submodel, a platform personalized order matching submodel, a platform personalized duration matching submodel, and a platform personalized reward calculation submodel;

wherein, the inputting the target service data into the platform personalized order receiving reward model to obtain the driver's order receiving reward amount comprises:

inputting the target service data into the platform personalized reward object matching sub-model to obtain first service data; the first service data is service data which accords with the limit condition of the platform personalized order receiving reward object;

inputting the first service data into the platform personalized order matching sub-model and the platform personalized duration matching sub-model to obtain second service data; the second service data is the service data which accords with the limitation condition of the platform personalized order receiving reward order and the limitation condition of the platform personalized order receiving reward duration in the first service data;

and inputting the second service data into the platform personalized reward calculation submodel to obtain the sum of the driver's bill receiving reward.

9. The method of claim 8, wherein the platform personalized reward calculation submodel includes a monetary calculation model and a risk control model;

wherein, the inputting the second service data into the platform personalized reward calculation submodel to obtain the driver's receiving reward amount comprises:

inputting the second service data into a money calculation model to obtain the reward money of the driver for issuing the receiving order;

if the reward amount of the receiving order to be issued of the driver is larger than or equal to the bottom-guaranteed reward amount of the driver, determining the reward amount of the receiving order to be issued of the driver as the reward amount of the receiving order of the driver;

if the reward amount of the to-be-issued receipt of the driver is smaller than the bottom-guaranteed reward amount of the driver, counting the total business flow amount of the driver according to the second business data, and inputting the total business flow amount of the driver into a risk control model to obtain a wind control proportion; the wind control proportion is the proportion of the total business flow sum of the driver and the bottom-guaranteed reward amount of the driver;

if the wind control proportion is larger than or equal to a proportion threshold value, determining the bill receiving reward amount of the driver according to the difference between the bottom-protected reward amount of the driver and the bill receiving reward amount to be issued of the driver;

and if the wind control proportion is smaller than a proportion threshold value, outputting a manual audit prompt notice.

10. The method of claim 7, wherein the platform personalized pick up reward model comprises a platform personalized campaign visible object matching submodel; the method further comprises the following steps:

inputting the target business data into the platform personalized activity visible object matching sub-model, and determining third business data; the third service data is service data which accords with the visible object limitation condition of the platform personalized order receiving reward activity;

and outputting a display control instruction to a corresponding user terminal according to the third service data so that the user terminal displays the propaganda information of the order-receiving reward activity according to the display control instruction.

11. The method of claim 7, wherein the obtaining target service data comprises:

acquiring network car booking service log data;

and performing data extraction on the network appointment service log data to obtain the target service data.

12. An order-receiving reward model generation apparatus, the apparatus comprising:

the data determination module is used for responding to selection operation on configuration data of the model configuration interface and determining model attribute data according to the result of the selection operation;

the data acquisition module is used for acquiring a preset multi-branch tree model; the multi-branch tree model comprises a preset rewarding object matching sub-model, a preset order matching sub-model, a preset duration matching sub-model, a preset rewarding calculation sub-model and a preset activity visible object matching sub-model;

the traversal cutting module is used for performing traversal cutting processing on the preset multi-branch tree model according to the model attribute data to generate a bill receiving reward model; and the order receiving reward model is used for the service platform management server to count the order receiving reward amount of the driver according to the online car booking service data.

13. A system for generating a platform-personalized pick-up reward model, the system comprising:

model receiving means for receiving the order taking reward model generated by the order taking reward model generating means of claim 12 sent by the configuration center server;

and the model generating device is used for responding to the parameter setting operation of the order receiving reward model and generating a platform personalized order receiving reward model according to the result of the parameter setting operation and the order receiving reward model.

14. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method for generating a pick-up reward model according to any one of claims 1 to 5 or the method for generating a platform-personalized pick-up reward model according to any one of claims 6 to 11 are implemented by the processor when executing the computer program.

15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the order taking reward model generation method of any one of claims 1 to 5 or the platform-personalized order taking reward model generation method of any one of claims 6 to 11.