CN111898784A

CN111898784A - Method, electronic device, and storage medium for vehicle rental reservation

Info

Publication number: CN111898784A
Application number: CN202011047801.5A
Authority: CN
Inventors: 朱广; 章瑞平; 谢春
Original assignee: Shanghai Ehi Auto Services Co ltd; Nanjing Wenhang Automobile Technology Co ltd
Current assignee: Shanghai Ehi Auto Services Co ltd; Nanjing Wenhang Automobile Technology Co ltd
Priority date: 2020-09-29
Filing date: 2020-09-29
Publication date: 2020-11-06

Abstract

Embodiments of the present disclosure relate to methods, apparatuses, and media for vehicle rental reservation, and relate to the field of information processing. According to the method, if a rental request related to a first vehicle rental store is received, a set of predicted vehicle rental order quantities is determined based on historical order data; determining a super sale rate set based on the historical vehicle rental order cancellation rate set, the maximum vehicle inventory quantity set and the predicted vehicle rental order quantity set; determining a virtual available vehicle inventory quantity set based on the over-sale rate set, the maximum vehicle inventory quantity set and the existing vehicle rental order quantity set; if it is determined that the number of virtual available vehicle inventory at the first vehicle rental store is less than or equal to zero: determining a second vehicle rental store; and transmitting an instruction to dispatch the vehicle from the second vehicle rental store to the first vehicle rental store. Thus, overdischarge can be performed in consideration of the cancellation rate, stock efficiency is improved, and mismatching between orders and stocks can be avoided by inter-shop vehicle scheduling.

Description

Method, electronic device, and storage medium for vehicle rental reservation

Technical Field

Embodiments of the present disclosure relate generally to the field of information processing, and more particularly, to a method, electronic device, and computer storage medium for vehicle rental reservation.

Background

The traditional vehicle rental inventory calculation is used for calculating the inventory split of different vehicle rental stores. If unbalanced booking of different vehicle rental stores occurs, partial stores are insufficient in inventory and partial stores are excessive in inventory. In addition, conventional inventory calculations calculate the available inventory based on data conditions that have occurred. After the pre-sale of all the stocks is finished, the stocks cannot be continuously sold. The cancellation of the order exists as a normal state, and after the order is cancelled, the actual sales rate is reduced.

Disclosure of Invention

Provided are a method, an electronic device, and a computer storage medium for vehicle rental reservation capable of over-selling in consideration of a cancellation rate, improving inventory efficiency, and avoiding mismatching of orders and inventory through inter-shop vehicle scheduling.

According to a first aspect of the present disclosure, a method for vehicle rental reservation is provided. The method comprises the following steps: if it is determined that a rental request associated with the first vehicle rental store for the first model and the first time is received from the user device, determining a set of predicted vehicle rental order quantities associated with the group of vehicle rental stores for the first model and the first time based on historical vehicle rental order data for the first model associated with the group of vehicle rental stores to which the first vehicle rental store belongs; determining a set of oversell rates associated with the group of vehicle rental stores for the first model and the first time based on the set of historical vehicle rental order cancellation rates for the first model associated with the group of vehicle rental stores, the set of maximum vehicle inventory quantities for the first model associated with the group of vehicle rental stores, and the set of predicted vehicle rental order quantities; determining a set of virtual available vehicle inventory quantities for the first model and the first time associated with the group of vehicle rental stores based on the set of upsell rates, the set of maximum vehicle inventory quantities, and the set of existing vehicle rental order quantities for the first model and the first time associated with the group of vehicle rental stores; if the virtual available vehicle inventory number associated with the first vehicle rental store is determined to be greater than zero, sending a vehicle rental reservation instruction to a terminal device associated with the first vehicle rental store; and if it is determined that the virtual available vehicle inventory quantity associated with the first vehicle rental store is less than or equal to zero: determining a second vehicle rental store from the group of vehicle rental stores, wherein the amount of virtual available vehicle inventory associated with the second vehicle rental store is greater than zero and the second vehicle rental store is closest to the first vehicle rental store in the group of vehicle rental stores; and sending instructions to the vehicle dispatching system regarding dispatching the vehicle of the first vehicle type from the second vehicle rental store to the first vehicle rental store.

According to a second aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

In a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements a method according to the first aspect of the present disclosure.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.

FIG. 1 is a schematic diagram of an information handling environment 100 according to an embodiment of the present disclosure.

Fig. 2 is a schematic diagram of a method 200 for vehicle rental reservation, according to an embodiment of the disclosure.

FIG. 3 is a schematic diagram of a method 300 for determining a set of predicted vehicle rental order quantities, according to an embodiment of the disclosure.

Fig. 4 is a schematic diagram of a method 400 for determining a set of upsell rates in accordance with an embodiment of the present disclosure.

Fig. 5 is a schematic diagram of a method 500 for determining a set of upsell rates in accordance with an embodiment of the present disclosure.

FIG. 6 is a schematic block diagram of a method 600 for determining a set of virtual available vehicle inventory amounts in accordance with an embodiment of the present disclosure.

Fig. 7 is a block diagram of an electronic device used to implement a method for vehicle rental reservation of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.

As described above, the conventional scheme does not share the inventory between the rental car shops and does not consider the case of the order cancellation rate, resulting in inefficiency of the inventory.

To address, at least in part, one or more of the above issues and other potential issues, an example embodiment of the present disclosure proposes a solution for vehicle rental reservation. In this scenario, the computing device determines a set of predicted vehicle rental order quantities associated with a group of vehicle rental stores for the first type and the first time based on historical vehicle rental order data for the first type associated with the group of vehicle rental stores to which the first vehicle rental store belongs if it is determined that a rental request associated with the first vehicle rental store for the first type and the first time is received from the user device. The computing device then determines a set of overdesidue rates associated with the group of vehicle rental stores for the first model and the first time based on the set of historical vehicle rental order cancellation rates for the first model associated with the group of vehicle rental stores, the set of maximum vehicle inventory quantities for the first model associated with the group of vehicle rental stores, and the set of predicted vehicle rental order quantities. Next, the computing device determines a set of virtual available vehicle inventory quantities for the first model and the first time associated with the group of vehicle rental stores based on the set of upsell rates, the set of maximum vehicle inventory quantities, and the set of existing vehicle rental order quantities for the first model and the first time associated with the group of vehicle rental stores. The computing device sends a vehicle rental reservation instruction to a terminal device associated with the first vehicle rental store if it is determined that the number of virtual available vehicle inventory associated with the first vehicle rental store is greater than zero. The computing device, if it is determined that the virtual available vehicle inventory quantity associated with the first vehicle rental store is less than or equal to zero: determining a second vehicle rental store from the group of vehicle rental stores, wherein the amount of virtual available vehicle inventory associated with the second vehicle rental store is greater than zero and the second vehicle rental store is closest to the first vehicle rental store in the group of vehicle rental stores; and sending instructions to the vehicle dispatching system regarding dispatching the vehicle of the first vehicle type from the second vehicle rental store to the first vehicle rental store. In this way, the vehicle rental can be oversubscribed in consideration of the historical vehicle rental order cancellation rate, so that the actual order quantity after the order cancellation and the actual inventory match as much as possible, thereby improving the inventory efficiency, and avoiding the mismatch of the vehicle rental order and the inventory through the vehicle scheduling in the vehicle rental store group to which the target vehicle rental store belongs.

Hereinafter, specific examples of the present scheme will be described in more detail with reference to the accompanying drawings.

FIG. 1 shows a schematic diagram of an example of an information processing environment 100, according to an embodiment of the present disclosure. The information processing environment 100 can include a computing device 110, a user device 120, a vehicle dispatch system 130, a terminal device 140 associated with a first vehicle rental store.

The computing device 110 includes, for example, but is not limited to, a server computer, a multiprocessor system, a mainframe computer, a distributed computing environment including any of the above systems or devices, and the like. In some embodiments, the computing device 110 may have one or more processing units, including special purpose processing units such as image processing units GPU, field programmable gate arrays FPGA, and application specific integrated circuits ASIC, and general purpose processing units such as central processing units CPU.

The user device 120 and the terminal device 140 include, for example, but are not limited to, personal computers, desktop computers, tablet computers, laptop computers, smart phones, personal digital assistants, and the like.

The vehicle dispatching system 130 can be used to dispatch vehicles between vehicle rental stores.

The computing device 110 is to determine a set of predicted vehicle rental order quantities associated with the group of vehicle rental stores for the first type and the first time based on historical vehicle rental order data for the first type associated with the group of vehicle rental stores to which the first vehicle rental store belongs if it is determined that a rental request associated with the first vehicle rental store for the first type and the first time is received from the user device 120; determining a set of oversell rates associated with the group of vehicle rental stores for the first model and the first time based on the set of historical vehicle rental order cancellation rates for the first model associated with the group of vehicle rental stores, the set of maximum vehicle inventory quantities for the first model associated with the group of vehicle rental stores, and the set of predicted vehicle rental order quantities; determining a set of virtual available vehicle inventory quantities for the first model and the first time associated with the group of vehicle rental stores based on the set of upsell rates, the set of maximum vehicle inventory quantities, and the set of existing vehicle rental order quantities for the first model and the first time associated with the group of vehicle rental stores; if it is determined that the number of virtual available vehicle inventory associated with the first vehicle rental store is greater than zero, transmitting a vehicle rental reservation instruction to the terminal device 140 associated with the first vehicle rental store; and if it is determined that the virtual available vehicle inventory quantity associated with the first vehicle rental store is less than or equal to zero: determining a second vehicle rental store from the group of vehicle rental stores, wherein the amount of virtual available vehicle inventory associated with the second vehicle rental store is greater than zero and the second vehicle rental store is closest to the first vehicle rental store in the group of vehicle rental stores; and sending instructions to the vehicle dispatching system 130 regarding dispatching the vehicle of the first vehicle type from the second vehicle rental store to the first vehicle rental store.

Therefore, the vehicle rental can be oversaled by considering the historical vehicle rental order cancellation rate, so that the actual order quantity after the order cancellation is matched with the actual inventory as much as possible, the inventory efficiency is improved, and the mismatching of the vehicle rental order and the inventory is avoided through vehicle scheduling in the vehicle rental store group to which the target vehicle rental store belongs.

Fig. 2 shows a flow diagram of a method 200 for vehicle rental reservation, according to an embodiment of the disclosure. For example, the method 200 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 200 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the present disclosure is not limited in this respect.

At block 202, the computing device 110 determines whether a rental request associated with the first vehicle rental store for the first vehicle type and the first time is received from the user device 120.

The first vehicle type includes, for example, but not limited to, a sedan, an SUV, an off-road vehicle, and the like. Additionally, the first vehicle model may also include a specific vehicle make and model, such as the XX brand XX model sedan.

The first time may include, for example, but is not limited to, a date, such as 10/month 1/2020, or a time period, such as 10/month 1/10/month 7/2020.

If the computing device 110 determines at block 202 that a rental request associated with the first vehicle rental store for the first model and the first time is received from the user device 120, then at block 204 a set of predicted vehicle rental order quantities associated with the group of vehicle rental stores to which the first vehicle rental store belongs for the first model and the first time is determined based on historical vehicle rental order data for the first model associated with the group of vehicle rental stores.

The group of vehicle rental stores to which the first vehicle rental store belongs may include a plurality of vehicle rental stores, e.g., 5, 10, etc. The method for determining the group of vehicle rental stores to which the first vehicle rental store belongs will be described in detail below.

The historical vehicle rental order data includes, for example, rental store, rental vehicle type, rental time, and the like data, such as vehicle rental order data for the past year.

In some embodiments, the predicted number of vehicle rental orders for a first model and a first time for a same store may be determined by the rental rate of the store for the first model at a historical contemporaneous first time. For example, if the rental rate of the first vehicle type on day 10, month and 1 of the last year is 80%, the rental rate is increased or decreased by a floating interval, for example, 5%, the rental rate of the first vehicle type on day 10, month and 1 of the present year can be obtained, for example, 85%, and if the maximum vehicle inventory is, for example, 100, the number of vehicle rental orders is predicted to be 85. The float interval may be fixed or determined based on annual order growth rate, for example, based on the ratio of the number of actual vehicle rental orders in the previous 9 months of the year to the number of actual vehicle rental orders in the previous 9 months of the year.

The method for determining the set of predicted vehicle rental order quantities is described in detail below in conjunction with FIG. 3.

At block 206, the computing device 110 determines a set of over-sales rates associated with the group of vehicle rental stores for the first style and the first time based on the set of historical vehicle rental order cancellation rates for the first style associated with the group of vehicle rental stores, the set of maximum vehicle inventory quantities for the first style associated with the group of vehicle rental stores, and the set of predicted vehicle rental order quantities.

The historical vehicle rental order cancellation rate may include, for example and without limitation, an order cancellation rate for a historical contemporaneous first time period, such as a historical vehicle rental day order cancellation rate, which may be calculated as a historical vehicle rental day order cancellation quantity divided by a historical vehicle rental day order total quantity. For example, if the first time is day 10/1 this year, the historical vehicle rental order cancellation rate may be the vehicle rental order cancellation rate of day 10/1 the last year. The historical vehicle rental order cancellation rate may also include, for example, a historical average order cancellation rate, such as an average order cancellation rate over the last year.

The maximum vehicle inventory quantity may be understood as the quantity of vehicle inventory originally configured in the vehicle rental store, and may include the quantity of vehicle inventory actually available in the vehicle rental store and the quantity of existing vehicle rental orders. For example, the maximum vehicle inventory quantity of a certain vehicle type in a certain store is 100, the quantity of existing rental orders of a first vehicle type is 30, and the quantity of actual available vehicle inventory in the store of the first vehicle type is 70.

The method for determining the over-sale rate set will be described in detail below in conjunction with fig. 4 and 5.

At block 208, the computing device 110 determines a set of virtual available vehicle inventory quantities for the first model and the first time associated with the group of vehicle rental stores based on the set of upsell rates, the set of maximum vehicle inventory quantities, and the set of existing vehicle rental order quantities for the first model and the first time associated with the group of vehicle rental stores.

For example, a set of oversubscribed quantities may be determined based on a set of oversubscribed rates and a set of maximum vehicle inventory quantities, and a set of virtual available vehicle inventory quantities may be determined from the set of oversubscribed quantities, the set of maximum vehicle inventory quantities, and the set of existing vehicle rental order quantities.

The method for determining the set of virtual available vehicle inventory amounts is described in detail below in conjunction with FIG. 6.

At block 210, the computing device 110 determines whether the virtual available vehicle inventory amount associated with the first vehicle rental store is greater than zero.

If the computing device 110 determines at block 210 that the number of virtual available vehicle inventory associated with the first vehicle rental store is greater than zero, then at block 212 a vehicle rental reservation instruction is sent to the terminal device 140 associated with the first vehicle rental store. Computing device 110 may also then send an acknowledgement to user device 120 for the lease request.

If the computing device 110 determines at block 210 that the virtual amount of available vehicle inventory associated with the first vehicle rental store is less than or equal to zero, a second vehicle rental store is determined at block 214 from the group of vehicle rental stores, wherein the virtual amount of available vehicle inventory associated with the second vehicle rental store is greater than zero and the second vehicle rental store is closest to the first vehicle rental store in the group of vehicle rental stores.

At block 216, the computing device 110 sends instructions to the vehicle dispatching system 130 regarding dispatching a vehicle of the first vehicle type from the second vehicle rental store to the first vehicle rental store. Computing device 110 may also then send an acknowledgement to user device 120 for the lease request.

In some embodiments, the computing device 110 may also determine whether the sets of virtual available vehicle inventory quantities associated with the group of vehicle rental stores are each less than or equal to zero, and send an instruction to the vehicle dispatching system regarding dispatching a vehicle of the first vehicle type from the vehicle spare inventory points associated with the group of vehicle rental stores to the first vehicle rental store if the computing device 110 determines that the sets of virtual available vehicle inventory quantities associated with the group of vehicle rental stores are each less than or equal to zero.

Thus, in the event that no virtual available vehicle inventory is available for a group of stores, a vehicle of the first vehicle type is dispatched from a vehicle backup inventory point associated with the group, thereby avoiding order fit to inventory.

Alternatively or additionally, in some embodiments, computing device 110 may determine an area where the first vehicle rental store is located; determining a set of distances between a set of vehicle rental stores and a first vehicle rental store within the area; determining a subset of vehicle rental stores from the set of vehicle rental stores, a distance between a vehicle rental store in the subset of vehicle rental stores and the first vehicle rental store being less than or equal to a maximum allowed inter-store distance associated with the region; and forming a vehicle rental store group by the subset of vehicle rental stores and the first vehicle rental store.

Thus, the vehicle rental store group to which the first vehicle rental store belongs can be determined as stores within the maximum allowable inter-store distance in the area where the first vehicle rental store is located, and the efficiency of vehicle scheduling in the group is improved.

FIG. 3 shows a flow diagram of a method 300 for determining a set of predicted vehicle rental order quantities, according to an embodiment of the disclosure. For example, the method 300 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.

At block 302, computing device 110 obtains, from historical vehicle rental order data, a first set of vehicle rental order quantities for a first vehicle type and a historical contemporaneous period for a first time associated with a group of vehicle rental stores.

The historical contemporaneous period of the first time is, for example, the last year contemporaneous period of the first time. The first time is, for example, 2020, 9, 25 days, and the historical contemporaneous period of the first time is 2019, 9, 25 days.

At block 304, the computing device 110 determines first weather information, a first holiday level, and a first discount rate for the first time.

The weather information may include, for example, weather types such as sunny, rainy, snowy, cloudy, etc., and information such as wind, temperature, etc.

Holiday ratings may be defined, for example, as follows: national day/spring festival, grade 5; minor and major (denier per quintuce/mid-autumn/Qingming), grade 4; students with chills and hots holidays, grade 3; ordinary weekends, grade 2; in the normal week, grade 1. For example, if the first time is 9 months and 25 days, the holiday level thereof is 1, if the first time is 9 months and 26 days, the holiday level thereof is 2, and if the first time is 10 months and 1 days, the holiday level thereof is 5.

The discount form may include, for example, but is not limited to, the following forms: immediately subtracting the amount type, namely subtracting 50 yuan immediately as follows; a rate discount type, such as a single rental car fee of 8; free days, such as the free first day of a new class; and fixing the format, such as renting 50 yuan for every day and day. Discount rate = discount amount/order amount.

At block 306, the computing device 110 determines second weather information, a second holiday level, and a second discount rate for the historical term.

At block 308, the computing device 110 determines a weather impact factor based on the first weather information, the second weather information, and a first linear regression model trained based on historical vehicle rental order data and weather information corresponding to the historical vehicle rental order data.

The historical vehicle rental order data may include, for example, the number of orders for a vehicle rental day for a plurality of years 2015-2019, the weather information corresponding thereto is, for example, the daily weather information for a plurality of years 2015-2019, and the first linear regression model may be trained by the ratio of the weather information for the same day in two years and the number of orders for the same day in two years 2015-2019.

At block 310, the computing device 110 determines a holiday impact factor based on the first holiday level, the second holiday level, and a second linear regression model trained based on historical vehicle rental order data and holiday level data corresponding to the historical vehicle rental order data.

The historical vehicle rental order data may include, for example, the number of orders for a vehicle rental day for a plurality of years 2015-2019, the holiday level data corresponding thereto may be, for example, the holiday level for each day for a plurality of years 2015-2019, and the second linear regression model may be trained by the ratio of the holiday level for the same day for two years and the number of orders for the same day for two years during 2015-2019.

At block 312, the computing device 110 determines the discount impact factor based on the first discount rate, the second discount rate, and a third linear regression model trained based on historical vehicle rental order data and discount rate data corresponding to the historical vehicle rental order data.

The historical vehicle rental order data may include, for example, the number of orders for the vehicle rental day in multiple years, for example, the number of orders for the vehicle rental day in 2015-2019, the discount rate data corresponding thereto is, for example, the discount rate corresponding to each day in 2015-2019, and a third linear regression model may be trained by the ratio of the discount rate for the same day in two years in 2015-2019 to the number of orders for the same day in two years.

At block 314, the computing device 110 weights the weather impact factor, the holiday impact factor, and the discount impact factor to obtain a total impact factor.

For example, total impact factor = weather impact factor + first weight + holiday impact factor + second weight + discount impact factor + third weight, the first weight, the second weight and the third weight may be preset.

At block 316, the computing device 110 determines a set of predicted vehicle rental order quantities based on the first set of vehicle rental order quantities and the overall impact factor.

For example, the first set of vehicle rental order quantities and the total impact factor are multiplied to obtain a set of vehicle rental order increments, and then the set of vehicle rental order increments is added to the first set of vehicle rental order quantities to obtain a set of predicted vehicle rental order quantities.

The specific formula can be calculated as follows: y = Y0 + Y0 [ [ f1 (X1, X1 ') ] θ 1 + f2 (X2, X2 ') ] θ 2 + f3 (X3, X3 ') ] θ 3], wherein Y represents a predicted vehicle rental order number for a first time, Y0 represents a vehicle rental order number for a historical period, f1 (X1, X1 ') represents a first linear regression model, X1 represents first weather information, X1 ' represents second weather information, θ 1 represents a first weight, f2 (X2, X2 ') represents a second linear regression model, X2 represents a first holiday level, X2 ' represents a second holiday level, θ 2 represents a second weight, f2 (X2, X2 ') represents a third linear regression model, X2 represents a first discount rate, X2 ' represents a third discount rate, and θ 2 represents a third discount rate.

Thus, the vehicle rental order number can be predicted more accurately in consideration of three factors affecting the order amount, such as weather, discount rate, and holidays.

FIG. 4 shows a flow chart of a method 400 for determining a set of upsell rates in accordance with an embodiment of the present disclosure. For example, the method 400 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 400 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.

At block 402, the computing device 110 determines a first subgroup of vehicle rental door stores and a second subgroup of vehicle rental door stores from the group of vehicle rental door stores, the predicted number of vehicle rental orders associated with the vehicle rental door stores in the first subgroup of vehicle rental door stores being greater than or equal to the maximum number of vehicle inventory associated with the vehicle rental door store, the predicted number of vehicle rental orders associated with the vehicle rental door stores in the second subgroup of vehicle rental door stores being less than the maximum number of vehicle inventory associated with the vehicle rental door store.

At block 404, the computing device 110 determines a first over-sale rate subset associated with the first vehicle rental door sub-group based on the historical vehicle rental order cancellation rate subset, the maximum vehicle inventory quantity subset, and the predicted vehicle rental order quantity subset associated with the first vehicle rental door sub-group.

At block 406, the computing device 110 sets a second subset of premium rates associated with the second vehicle rental-door store subgroup to all zeros.

At block 408, the computing device 110 combines the first and second super sales subsets into a super sales set.

Thus, the pre-sale can be performed for the stores whose predicted number of rental orders is greater than or equal to the maximum number of vehicle stocks, and not performed for the stores whose predicted number of rental orders is less than the maximum number of vehicle stocks, so that the pre-sale range of stores is more accurate.

FIG. 5 shows a flow diagram of a method 500 for determining a set of upsell rates in accordance with an embodiment of the present disclosure. For example, the method 500 may be performed by the computing device 110 as shown in fig. 1. It should be understood that method 500 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.

At block 502, the computing device 110 determines a set of predicted vehicle rental order cancellation quantities for the first vehicle type and the first time associated with the group of vehicle rental stores based on the set of historical vehicle rental order cancellation rates and the set of predicted vehicle rental order quantities.

For example, the set of historical vehicle rental order cancellation rates and the set of predicted vehicle rental order quantities may be multiplied by store to yield a set of predicted vehicle rental order cancellation quantities.

At block 504, the computing device 110 determines a set of oversubscription rates based on the set of predicted vehicle rental order cancellation quantities and the set of maximum vehicle inventory quantities.

For example, the set of predicted rental vehicle order cancellation quantities and the set of maximum vehicle inventory quantities may be divided by store to obtain a set of oversales.

Therefore, the predicted order cancellation quantity can be obtained by taking the historical order cancellation rate as the predicted order cancellation rate, the overdesing rate is determined based on the historical order cancellation rate, the cancelled order quantity is matched with the actual stock to the maximum extent according to the overdesing result, and the stock efficiency is improved.

In some embodiments, the computing device 110 may also determine whether the actual vehicle rental order cancellation quantity associated with the first vehicle rental store for the first vehicle type and the first time is less than the predicted vehicle rental order cancellation quantity associated with the first vehicle rental store. If the computing device 110 determines that the actual vehicle rental order cancellation amount associated with the first vehicle rental store for the first model and the first time is less than the predicted vehicle rental order cancellation amount associated with the first vehicle rental store, it is determined whether the existing vehicle rental order amount associated with the first vehicle rental store for the first model and the first time is greater than the maximum vehicle inventory amount associated with the first vehicle rental store.

In still other embodiments, the computing device may also determine whether an existing quantity of vehicle rental orders associated with the first vehicle rental store for the first vehicle type and the first time is greater than a predicted quantity of vehicle rental orders associated with the first vehicle rental store. If the computing device 110 determines that the existing quantity of vehicle rental orders associated with the first vehicle rental store for the first vehicle type and the first time is greater than the predicted quantity of vehicle rental orders associated with the first vehicle rental store, it is determined whether the existing quantity of vehicle rental orders is greater than a maximum quantity of vehicle inventory associated with the first vehicle rental store.

If computing device 110 determines that the existing quantity of vehicle rental orders is greater than the maximum quantity of vehicle inventory, then: setting the virtual available vehicle inventory amount associated with the first vehicle rental store to be equal to an actual available vehicle inventory amount associated with the first vehicle rental store for the first model and the first time; and prompting information regarding cancellation of over-sale for the first vehicle type and the first time associated with the first vehicle rental store.

Therefore, when the actual order cancellation amount is less than the expected amount or the actual order amount exceeds the expected amount and the actual order amount exceeds the maximum inventory, the overdischarge can be automatically cancelled and prompted, so that the situation that the actual inventory cannot accommodate the order due to the overdischarge is avoided, and the user experience is improved.

FIG. 6 shows a flow diagram of a method 600 for determining a set of virtual available vehicle inventory amounts, in accordance with an embodiment of the present disclosure. For example, the method 600 may be performed by the computing device 110 as shown in FIG. 1. It should be understood that method 600 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.

At block 602, the computing device 110 determines a set of over-sales quantities for a first model and a first time associated with a group of vehicle rental stores based on the set of over-sales rates and the set of maximum vehicle inventory quantities.

For example, the set of over-sales rates and the set of maximum vehicle inventory quantities may be multiplied by store to obtain a set of over-sales quantities.

At block 604, the computing device 110 determines a set of virtual maximum vehicle inventory quantities for the first model and the first time associated with the group of vehicle rental stores based on the set of over-sales quantities and the set of maximum vehicle inventory quantities.

For example, the set of over-sales quantities and the set of maximum vehicle inventory quantities may be added by store to obtain a set of virtual maximum vehicle inventory quantities.

At block 606, the computing device 110 determines a set of virtual available vehicle inventory quantities based on the set of virtual maximum vehicle inventory quantities and the set of existing vehicle rental order quantities.

For example, a set of virtual available vehicle inventory quantities may be obtained by store-by-store subtraction of a set of virtual maximum vehicle inventory quantities and a set of existing vehicle rental order quantities.

Therefore, the virtual available vehicle inventory quantity set under the overspill condition can be determined, and the overspill processing of the vehicle leasing reservation is facilitated.

Fig. 7 illustrates a schematic block diagram of an example device 700 that may be used to implement embodiments of the present disclosure. For example, computing device 110 as shown in FIG. 1 may be implemented by device 700. As shown, device 700 includes a Central Processing Unit (CPU) 701 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 702 or computer program instructions loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the device 700 can also be stored. The CPU 701, the ROM 702, and the RAM703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in the device 700 are connected to the I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, a microphone, and the like; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The various processes and processes described above, such as the

method

200 and 600, may be performed by the central processing unit 701. For example, in some embodiments, the method 200-600 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 700 via ROM 702 and/or communications unit 709. When the computer program is loaded into the RAM703 and executed by the CPU 701, one or more acts of the

method

200 and 600 described above may be performed.

The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for vehicle rental reservation, comprising:

if it is determined that a rental request associated with a first vehicle rental store for a first vehicle type and a first time is received from a user device, determining a set of predicted vehicle rental order quantities associated with the group of vehicle rental stores for the first vehicle type and the first time based on historical vehicle rental order data for the first vehicle type associated with the group of vehicle rental stores to which the first vehicle rental store belongs;

determining a set of overdesills associated with the group of vehicle rental stores for the first vehicle type and the first time based on the set of historical vehicle rental order cancellation rates for the first vehicle type associated with the group of vehicle rental stores, the set of maximum vehicle inventory quantities for the first vehicle type associated with the group of vehicle rental stores, and the set of predicted vehicle rental order quantities;

determining a set of virtual available vehicle inventory quantities associated with the group of vehicle rental stores for the first vehicle type and the first time based on the set of upsell rates, the set of maximum vehicle inventory quantities, and a set of existing vehicle rental order quantities associated with the group of vehicle rental stores for the first vehicle type and the first time;

transmitting a vehicle rental reservation instruction to a terminal device associated with the first vehicle rental store if it is determined that the number of virtual available vehicle inventories associated with the first vehicle rental store is greater than zero; and

if it is determined that the virtual available vehicle inventory quantity associated with the first vehicle rental store is less than or equal to zero:

determining a second vehicle rental store from the group of vehicle rental stores, wherein the amount of virtual available vehicle inventory associated with the second vehicle rental store is greater than zero and the second vehicle rental store is closest to the first vehicle rental store in the group of vehicle rental stores; and

sending instructions to a vehicle dispatch system regarding dispatching a vehicle of the first vehicle type from the second vehicle rental store to the first vehicle rental store.

2. The method of claim 1, wherein determining the set of predicted vehicle rental order quantities comprises:

obtaining, from the historical vehicle rental order data, a first set of vehicle rental order quantities for the first vehicle type and historical contemporaneous for the first time associated with the group of vehicle rental stores;

determining first weather information, a first holiday grade and a first discount rate of the first time;

determining second weather information, a second holiday grade and a second discount rate of the historical synchronization;

determining a weather influence factor based on the first weather information, the second weather information and a first linear regression model, wherein the first linear regression model is trained based on the historical vehicle rental order data and weather information corresponding to the historical vehicle rental order data;

determining a holiday impact factor based on the first holiday level, the second holiday level and a second linear regression model, wherein the second linear regression model is trained based on the historical vehicle rental order data and holiday level data corresponding to the historical vehicle rental order data;

determining a discount impact factor based on the first discount rate, the second discount rate and a third linear regression model, wherein the third linear regression model is trained based on the historical vehicle rental order data and discount rate data corresponding to the historical vehicle rental order data;

weighting the weather influence factor, the holiday influence factor and the discount influence factor to obtain a total influence factor; and

determining the set of predicted vehicle rental order quantities based on the first set of vehicle rental order quantities and the overall impact factor.

3. The method of claim 1, wherein determining the set of upsell rates comprises:

determining, from the group of vehicle rental stores, a first subgroup of vehicle rental stores and a second subgroup of vehicle rental stores, a predicted number of vehicle rental orders associated with vehicle rental stores in the first subgroup of vehicle rental stores being greater than or equal to a maximum number of vehicle inventory associated with the vehicle rental stores, a predicted number of vehicle rental orders associated with vehicle rental stores in the second subgroup of vehicle rental stores being less than the maximum number of vehicle inventory associated with the vehicle rental stores;

determining a first oversubscription rate subset associated with the first vehicle rental door subgroup based on the historical vehicle rental order cancellation rate subset, the maximum vehicle inventory quantity subset, and the forecast vehicle rental order quantity subset associated with the first vehicle rental door subgroup;

setting a second subset of oversubscription rates associated with the second subgroup of vehicle rental doors to all zeros; and

and combining the first super sale rate subset and the second super sale rate subset into the super sale rate set.

4. The method of claim 1, wherein determining the set of upsell rates comprises:

determining a set of predicted vehicle rental order cancellation quantities for the first vehicle type and the first time associated with the group of vehicle rental stores based on the set of historical vehicle rental order cancellation rates and the set of predicted vehicle rental order quantities; and

determining the set of oversubscription rates based on the set of predicted vehicle rental order cancellation quantities and the set of maximum vehicle inventory quantities.

5. The method of claim 4, further comprising:

determining whether the existing vehicle rental order quantity is greater than the maximum vehicle inventory quantity associated with the first vehicle rental store if it is determined that the actual vehicle rental order cancellation quantity associated with the first vehicle type and the first time is less than the predicted vehicle rental order cancellation quantity associated with the first vehicle rental store or it is determined that the existing vehicle rental order quantity associated with the first vehicle rental store for the first vehicle type and the first time is greater than the predicted vehicle rental order quantity associated with the first vehicle rental store; and

if it is determined that the quantity of existing vehicle rental orders is greater than the maximum quantity of vehicle inventory, then:

setting a virtual available vehicle inventory quantity associated with the first vehicle rental store equal to an actual available vehicle inventory quantity associated with the first vehicle rental store for the first vehicle type and the first time; and

prompting information regarding cancellation of oversalelling associated with the first vehicle rental store for the first vehicle type and the first time.

6. The method of claim 1, wherein determining the set of virtual available vehicle inventory quantities comprises:

determining a set of over-sales quantities associated with the group of vehicle rental stores for the first vehicle type and the first time based on the set of over-sales rates and the set of maximum vehicle inventory quantities; and

determining a set of virtual maximum vehicle inventory quantities associated with the group of vehicle rental stores for the first vehicle type and the first time based on the set of over-sales quantities and the set of maximum vehicle inventory quantities; and

determining the set of virtual available vehicle inventory quantities based on the set of virtual maximum vehicle inventory quantities and the set of existing vehicle rental order quantities.

7. The method of claim 1, further comprising:

if it is determined that the set of virtual available vehicle inventory quantities associated with the group of vehicle rental stores are all less than or equal to zero, then instructions are sent to the vehicle dispatching system regarding dispatching a vehicle of the first vehicle type to the first vehicle rental store from a vehicle spare inventory point associated with the group of vehicle rental stores.

8. The method of claim 1, further comprising:

determining an area where the first vehicle rental store is located;

determining a set of distances between a set of vehicle rental stores within the area and the first vehicle rental store;

determining a subset of vehicle rental stores from the set of vehicle rental stores, a distance between a vehicle rental store in the subset of vehicle rental stores and the first vehicle rental store being less than or equal to a maximum allowed inter-store distance associated with the area; and

and forming the vehicle rental shop group by the subset of the vehicle rental shops and the first vehicle rental shop.

9. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.