Method, device, electronic device and storage medium for recommending online car-hailing models

Abstract

The present application provides a method, device, electronic device and storage medium for recommending online car-hailing vehicle models. The method obtains the pick-up location and service request time selected by the user to be responded to, obtains the number of queued orders before the service request time and within a preset distance range of the pick-up location, and recommends a target vehicle model in the reservation order of the user to be responded to according to the number of queued orders, so that the reservation order of the user to be responded to can be responded to by the target vehicle model after it is released, thereby accelerating the response speed of the order of the user to be responded to and improving the user experience.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to a method, device, electronic device and storage medium for recommending online car-hailing models. Background Art

[0002] At present, with the increasing convenience of transportation, online ride-hailing has become a common means of travel for people. The way to call an online ride-hailing is often to directly initiate an order through the relevant online ride-hailing application. The server processes the order and assigns a suitable online ride-hailing vehicle to the user.

[0003] During rush hours in the morning and evening and during rainy periods, the demand for online ride-hailing vehicles is relatively high. At this time, there are many orders for online ride-hailing vehicles, which can easily lead to an imbalance between supply and demand and cause a slow response to orders. Summary of the invention

[0004] In view of this, the purpose of this application is to provide a method, device, electronic device and storage medium for recommending online car-hailing models, so as to improve the response speed of orders during peak hours in the morning and evening, rainy periods, etc. by recommending models to users.

[0005] In a first aspect, an embodiment of the present invention provides a method for recommending online car-hailing models, comprising:

[0006] Get the pick-up location and service request time selected by the user to be responded to;

[0007] Obtaining the number of queued orders within a preset distance range of the pickup location before the service request time;

[0008] generating a reservation order according to the number of queued orders, the pick-up location and the service request time;

[0009] A target vehicle model is recommended in the reservation order of the user to be responded to according to the number of the queued orders.

[0010] In an optional implementation manner, the recommending a target vehicle model in the reservation order of the user to be responded to according to the number of the queued orders includes:

[0011] Determining whether the number of queued orders is greater than a first preset value;

[0012] If the number of queued orders is greater than the first preset value, a first target order and a second target order are obtained from the historical orders of the user to be responded to, wherein the first target order is the historical order that was published most recently and the number of queued orders when published is greater than the first preset value, and the second target order is the historical order that was published most recently by the user to be responded to;

[0013] According to the car model selected by the user to be responded to when issuing the first target order and the second target order, the corresponding target car model is recommended in the reservation order of the user to be responded to.

[0014] In an optional embodiment, the method further comprises:

[0015] If the number of the queued orders is not greater than the first preset value, obtaining the second target order from the historical orders of the user to be responded to;

[0016] According to the car model selected by the user to be responded to when issuing the second target order, a corresponding target car model is recommended in the reservation order of the user to be responded to.

[0017] In an optional embodiment, the method further comprises:

[0018] Determining whether the number of recommended target vehicle models reaches a second preset value;

[0019] If the second preset value is not reached, the number of the target vehicle models is supplemented to the second preset value.

[0020] In an optional implementation manner, the step of padding the number of the target vehicle models to the second preset value includes:

[0021] Calculate the number of vehicle models selected by the user to be responded to in the historical orders within a preset time range;

[0022] The target models are added in the order of the number of times each model is selected from most to least, until the number of the target models reaches a second preset value.

[0023] In an optional implementation manner, the step of padding the number of target vehicle models to a second preset value includes:

[0024] Calculate the number of times each vehicle model is selected in order information of other users within a preset time range of the service request time and within a preset distance range of the pickup location;

[0025] The target models are added in the order of the number of times each model is selected from most to least, until the number of the target models reaches a second preset value.

[0026] In an optional implementation manner, the target vehicle model is added in the order of the number of times each vehicle model is selected from most to least, including:

[0027] The backup recommended models that are not the target model are sequentially added as the target model in the order of the number of times the backup recommended models among the models are selected from most to least.

[0028] In a second aspect, an embodiment of the present invention provides a device for recommending online car-hailing models, comprising:

[0029] The first acquisition module is used to obtain the pick-up location and service request time selected by the user to be responded to;

[0030] A second acquisition module is used to acquire the number of queued orders within a preset distance range of the pick-up location before the service request time;

[0031] A reservation order generating module, used for generating a reservation order according to the number of queued orders, the boarding location and the service request time;

[0032] The vehicle model recommendation module is used to recommend a target vehicle model in the reservation order of the user to be responded to according to the number of the queued orders.

[0033] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method described in any of the aforementioned implementations.

[0034] In a fourth aspect, an embodiment of the present invention provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any of the aforementioned embodiments are executed.

[0035] The online car-hailing model recommendation method, device, electronic device and storage medium provided in the embodiments of the present application obtain the pick-up location and service request time selected by the user to be responded to, obtain the number of queued orders before the service request time and within a preset distance range of the pick-up location, and recommend a target model in the reservation order of the user to be responded to based on the number of queued orders, so that the reservation order of the user to be responded to can be responded to by the target model after it is released, thereby accelerating the response speed of the order of the user to be responded to and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A schematic diagram of the online car-hailing service system involved in this application;

[0038] Figure 2One of the flow charts of the method for recommending online car-hailing models provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of a pre-order page provided for an embodiment of the present application;

[0040] Figure 4 A flowchart of sub-steps of step S104 provided in an embodiment of the present application;

[0041] Figure 5 Flow chart 2 of the method for recommending online car-hailing models provided in an embodiment of the present application;

[0042] Figure 6 One of the sub-step flow charts of step S106 provided in the embodiment of the present application;

[0043] Figure 7 The second sub-step flowchart of step S106 provided in the embodiment of the present application;

[0044] Figure 8 A functional module diagram of a device for recommending online car-hailing models provided in an embodiment of the present application;

[0045] Fig. 9 A schematic diagram of an electronic device provided in an embodiment of the present application.

[0046] Icons: 100-online car-hailing service system; 110-server; 120-network; 130-passenger terminal; 140-driver terminal; 150-database; 10-electronic device; 11-processor; 12-memory; 13-bus; 200-online car-hailing model recommendation device; 210-first acquisition module; 220-second acquisition module; 230-reservation order generation module; 240-model recommendation module; 250-quantity judgment module; 260-model completion module. DETAILED DESCRIPTION

[0047] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0048] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0049] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0050] The terms "user", "passenger", "requester", "service requester" and "customer" in this application are used interchangeably to refer to individuals, entities or tools that can request or order services. The terms "driver", "provider", "service provider" and "supplier" in this application are used interchangeably to refer to individuals, entities or tools that can provide services. The term "user" in this application may refer to an individual, entity or tool that requests a service or orders a service. In this application, "user" and "passenger terminal" are used interchangeably, and "driver" and "driver terminal" are used interchangeably.

[0051] The terms "service request" and "order" in this application are used interchangeably to refer to a request initiated by a passenger, a service requester, a driver, a service provider, a supplier, etc., or any combination thereof. The "service request" or "order" may be accepted by a passenger, a service requester, a driver, a service provider, a supplier, etc., or any combination thereof. A service request may be charged or free of charge.

[0052] Please refer to Figure 1 , Figure 1 1 is a schematic diagram of an online car-hailing service system 100 involved in the present application. The online car-hailing service system 100 may be an online transportation service platform for transportation services such as taxis, chauffeur services, express cars, carpooling, bus services, driver rentals, or shuttle services, or any combination thereof. The online car-hailing service system 100 may include one or more of a server 110, a network 120, a passenger terminal 130, a driver terminal 140, and a database 150.

[0053] In some embodiments, the server 110 may include a processor 11. The processor 11 may process information and / or data related to the service request to perform one or more functions described in the present application. For example, the processor 11 may determine the target vehicle based on the service request obtained from the passenger terminal 130. In some embodiments, the processor 11 may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). By way of example only, the processor 11 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction set processor (ASIP), a graphics processing unit (GPU), a physical processing unit (PPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), or a microprocessor, or any combination thereof.

[0054] In some embodiments, the device types corresponding to the passenger terminal 130 and the driver terminal 140 may be mobile devices, such as smart home devices, wearable devices, smart mobile devices, virtual reality devices, or augmented reality devices, etc., or may be tablet computers, laptop computers, or built-in devices in motor vehicles, etc.

[0055] In some embodiments, the database 150 may be connected to the network 120 to communicate with one or more components in the online car-hailing service system 100 (e.g., the server 110, the passenger terminal 130, the driver terminal 140, etc.). One or more components in the online car-hailing service system 100 may access data or instructions stored in the database 150 via the network 120. In some embodiments, the database 150 may be directly connected to one or more components in the online car-hailing service system 100, or the database 150 may also be part of the server 110.

[0056] When a passenger needs an online car-hailing service, he or she selects the boarding location, alighting location, number of passengers, departure time, etc. through the passenger terminal 130 to generate a reservation order. The reservation order can display the estimated cost of the trip, the number of people in line at the current time point, and the estimated waiting time. Once the passenger confirms that everything is correct, the reservation order can be published and the order can be responded to.

[0057] Due to the high demand for online ride-hailing vehicles during peak hours in the morning and evening and during rainy periods, there are many orders for online ride-hailing vehicles at this time, but the number of vehicles available to serve passengers is far from enough, resulting in an imbalance between supply and demand, which will greatly slow down the response speed of orders. In order to solve this technical problem, the inventor found in his research that recommending models that can respond quickly to users based on the queue situation of online ride-hailing vehicles can greatly reduce the response time of orders and improve user experience.

[0058] The following is combined with the above Figure 1 The contents described in the online car-hailing service system 100 shown provide a detailed explanation of the online car-hailing model recommendation method provided in the embodiment of the present application.

[0059] Please refer to Figure 2 , Figure 2 This is one of the flow charts of the online car-hailing model recommendation method provided in the embodiment of the present application. In this embodiment, the online car-hailing model recommendation method is applied to the server 110 in the online car-hailing service system 100, and the method includes:

[0060] Step S101, obtaining the pick-up location and service request time selected by the user to be responded to.

[0061] Step S102, obtaining the number of queued orders within a preset distance range of the pick-up location before the service request time.

[0062] Step S103, generating a reservation order according to the number of queued orders, the boarding location and the service request time.

[0063] Step S104, recommending a target car model in the reservation order of the user to be responded to according to the number of queued orders.

[0064] In the above steps, the user to be responded to is a passenger who needs online car-hailing service. The passenger can select the boarding location, alighting location, departure time, number of passengers and other information on the passenger terminal 130. After the selection is completed, the reservation order information can be generated.

[0065] Exemplarily, the server 110 is in communication with the passenger terminal 130 to obtain the selection result of the user to be responded to (i.e., information such as the boarding location, alighting location, departure time, and number of passengers). After obtaining the selection result, the server 110 obtains the number of queued orders before the service request time and within a preset distance range of the boarding location based on the selection result, and generates a reservation order based on the number of queued orders. At the same time, the server 110 recommends the target vehicle model in the reservation order of the user to be responded to according to the number of queued orders, so that after the reservation order of the user to be responded to is released, it can be responded to by different vehicle models, thereby speeding up the response speed of the order of the user to be responded to and improving the user experience.

[0066] For example, in this embodiment, recommending a target car model in the reservation order of the user to be responded to according to the number of queued orders includes displaying the target car model recommended by the server 110 on the reservation order page of the passenger terminal 130. After analyzing the recommended target car model, the server 110 may also select the recommended car model by default. In addition, the reservation order page may also display information such as the boarding location, the alighting location, the departure time, the number of passengers, and the number of current queued orders returned by the server 110 and the estimated cost of the current trip.

[0067] Optionally, in this embodiment, the types of online car-hailing vehicles include: express, express, carpooling, private car, taxi, luxury car, premium car, business car, etc. Among them, the express car type is a car type that can quickly respond to user orders.

[0068] For example, after opening the online car-hailing application, the passenger can select or enter the pick-up location (location A), the drop-off location (location B), the departure time (depart immediately) and other information on the page. After receiving the user's selection result, the server 110 obtains the number of queued orders within a preset distance range (for example, within 3 km) of location A before the service request time (i.e., the current time), and generates a reservation order. Figure 3 As shown, Figure 3 A schematic diagram of a pre-order page provided in an embodiment of the present application. Figure 3 In the reservation, the passenger's boarding location (location A), alighting location (location B), departure time (departing now), number of queued orders (e.g. 10 people), estimated fare (e.g. 15 yuan), etc. are included in the reservation. Figure 3 The reservation order page shown may also display the target vehicle model recommended by the server 110 for the reservation order (eg, the default selected express vehicle model, carpool vehicle model, express vehicle model, and private vehicle model, etc.).

[0069] Based on the information displayed on the reservation order page, the user to be responded to can uncheck the recommended target vehicle model or keep it unchanged according to his or her needs, and confirm whether other information in the reservation order is correct. When the user to be responded to confirms that the information on the reservation order page is correct, he or she can click the confirmation call button on the reservation order page to send the order to server 110. After receiving the order, server 110 allocates a vehicle to the user according to the order information, thereby providing the user with online car-hailing service.

[0070] In this embodiment, the number of queued orders will affect the type of target vehicle type recommended. Figure 4 , Figure 4 The sub-step flow chart of step S104 provided in the embodiment of the present application. In this embodiment, step S104 includes:

[0071] Sub-step S1041, determining whether the number of queued orders is greater than a first preset value.

[0072] The number of queued orders refers to the number of orders whose calls have been confirmed before the service request time of the user to be responded to and within a preset distance range of the pick-up location of the user to be responded to.

[0073] For example, if the server 110 of the user to be responded to requests immediate departure and the boarding location is location A, the server 110 obtains the number of orders from other users whose boarding locations are within a preset distance range (e.g., 3 km) of location A before the current moment. For example, the number may be 15 orders, which means that there are already 15 orders that need to be allocated online ride-hailing vehicles around the boarding location of the user to be responded to.

[0074] The preset distance range is set because the range for searching for vehicles at two far-flung pick-up locations is different. For example, if location A is 10 kilometers away from location B, the vehicle assigned to the order corresponding to location A and the vehicle assigned to the order corresponding to location B are located in different areas and can be assigned at the same time without queuing.

[0075] In some implementations, the first preset value can be flexibly set, such as set to 10 or 20. Accordingly, it is first necessary to determine whether the number of orders waiting for response (the number of queued orders) in front of the user to be responded to is greater than 10 or 20.

[0076] It is worth noting that in other implementations of this embodiment, the preset distance range and the first preset value may also be other values, which are not specifically limited here.

[0077] Sub-step S1042, if the number of queued orders is greater than the first preset value, obtain the first target order and the second target order from the historical orders of the user to be responded to. The first target order is the historical order that was recently published and the number of queued orders is greater than the first preset value when it was published, and the second target order is the historical order that was recently published by the user to be responded to.

[0078] Sub-step S1043, recommending a corresponding target car model in the pre-order order of the user to be responded to according to the car model selected by the user to be responded to when issuing the first target order and the second target order.

[0079] In the above sub-steps, when the number of queued orders is greater than the first preset value, it means that the queuing phenomenon is more serious at this time. In order to speed up the response speed to the orders of the users to be responded to, the target model can be recommended to the users.

[0080] Exemplarily, the first target order and the second target order are first obtained from the historical orders of the user to be responded to, and the target car model is recommended to the user to be responded to according to the car model selected by the user to be responded to when issuing the first target order and the second target order.

[0081] Optionally, in this embodiment, the first target order and the second target order may be the same order or different orders.

[0082] For example, in the booking order information of passenger Zhang San, there are still 30 people waiting in line for this online car-hailing service, which is greater than the first preset value (for example, 20), so the server 110 searches for the first target order X and the second target order Y from Zhang San's historical orders. The first target order X refers to the historical order of Zhang San's most recent online car-hailing service with a queue number greater than 20; the second target order Y refers to Zhang San's previous order.

[0083] When the first target order X and the second target order Y are different orders, if Zhang San selects an express car model and a carpool car model in the first target order X released, and Zhang San selects an express car model and a carpool car model in the second target order Y released, then the target car models recommended for this booking order of passenger Zhang San are the express car model, the carpool car model and the express car model, which are displayed or checked by default on the booking order page of passenger Zhang San, and the remaining car models are folded and displayed on the booking order page.

[0084] When the first target order X and the second target order Y are the same order, that is, the number of queued orders of passenger Zhang San's previous order is greater than 20. The car model selected by passenger Zhang San in the previous order is recommended to the user as the target car model, and the target car model is displayed or selected by default on the reservation order page, and the remaining unrecommended car models can be folded and displayed on the reservation order page.

[0085] If passenger Zhang San did not select an express or carpooling model when the number of orders for which he had to wait in line was greater than 20 in the most recent booking, the express or carpooling model will not be recommended in this booking.

[0086] In addition, the target model is only a part of the model recommended by the server 110 according to the usage habits and queue conditions of the responding users, which does not mean that the users can only choose the recommended target model. The responding users can also choose the model they need to take according to their own needs.

[0087] In the above embodiment, by using the model selected by the user to be responded in the first target order and the second target order as the target model recommended for the user, the response efficiency can be improved while the recommended target model can be made more in line with the user's usage habits, avoiding the recommendation of uncommon models for the user. In addition, it is also possible to determine whether the user wants to be responded to quickly based on whether the user checked the express model in the order when he needed to queue last time, so that the recommended target model is more in line with the user, thereby improving the user experience.

[0088] Please continue to refer to Figure 4 In this embodiment, if the number of queued orders is not greater than the first preset value, the online car-hailing model recommendation method further includes:

[0089] Sub-step S1044, obtaining a second target order from the historical orders of the user to be responded to.

[0090] Sub-step S1045, recommending a corresponding target car model in the reservation order of the user to be responded to according to the car model selected by the user to be responded to when issuing the second target order.

[0091] In the above sub-steps, the number of queued orders may be greater than 0 but not greater than the first preset value (e.g., not greater than 20 orders). It can be understood that calling an online car-hailing service requires queuing at this time, but the number of orders from other users waiting for a response is small and the waiting time is short. At this time, the second target order can be directly obtained from the historical orders of the user to be responded to, without obtaining the first target order. Finally, the model selected by the user to be responded to in the second target order is used as the target model recommended by the user.

[0092] For example, if the number of queued orders in the reservation order of passenger Li Si is 5, which does not exceed the first preset value (20), it means that the difference between the order request at the current moment and the number of vehicles is not large, and the user does not need to wait for a long time. The server 110 directly obtains the previous order in the historical order of passenger Li Si (that is, the second target order).

[0093] If the vehicle type selected by passenger Li Si in the previous order is an express vehicle type and a carpooling vehicle type, the server 110 recommends the target vehicle type as an express vehicle type and a carpooling vehicle type in the current booking page of the passenger. After confirming that the booking information is correct, the passenger clicks the confirmation call, generates a service request order and sends it to the server 110. After receiving the service request order, the server 110 allocates a vehicle to the passenger.

[0094] Optionally, the server 110 can usually recommend multiple target car models for passengers. When the types of target car models recommended based on the car models selected by the responding user in the historical orders are small, for example, each order in the passenger's historical orders only selected one car model, and there is only one target car model recommended in the end, the number of target car models needs to be supplemented at this time.

[0095] For example, please refer to Figure 5 , Figure 5 Flow chart 2 of the method for recommending online car-hailing models provided in the embodiment of the present application. In this embodiment, the method further includes:

[0096] Step S105, determining whether the number of recommended target vehicle models reaches a second preset value.

[0097] Step S106: If the second preset value is not reached, the number of target vehicle models is increased to the second preset value.

[0098] In one embodiment, for example, if the vehicle models selected by the user to be responded to in the first target order and the second target order are express vehicle models, carpooling vehicle models and express vehicle models, the vehicle models recommended by the server 110 for the current reservation order of the user to be responded to are also express vehicle models, carpooling vehicle models and express vehicle models.

[0099] When the second preset value is 4, that is, the number of target vehicle models that can be displayed in the reservation order page is 4. In the above example, since the number of recommended target vehicle models is only 3, which is less than 4, the number of recommended target vehicle models needs to be supplemented to 4.

[0100] Of course, in other implementations of this embodiment, the number of target vehicle models recommended by the server 110 may also be other values, or the second preset value may be other values, which are not specifically limited here.

[0101] For example, please refer to Figure 6 , Figure 6 One of the sub-step flow charts of step S106 provided in the embodiment of the present application. In this embodiment, step S106 includes:

[0102] Sub-step S1061, calculating the number of vehicle models selected by the responding user in historical orders within a preset time range.

[0103] Sub-step S1062, adding target models in the order of the number of times each model is selected from most to least, until the number of target models reaches a second preset value.

[0104] During the implementation process, the server 110 obtains the historical orders of the user to be responded to within a preset time range (for example, within a month or a week) from the database 150, and calculates the number of times the user to be responded to select each model in the historical orders within this time period, and adds target models in order from the most to the least number of times selected until the number of target models reaches a second preset value.

[0105] For example, a passenger has 20 historical orders in a month. Among these 20 historical orders, the passenger selected express car models 20 times, express car models 15 times, private car models 10 times, carpooling models 5 times, and luxury car models 2 times.

[0106] The target models are added in the order of the number of times each model is selected from most to least until the number of target models reaches the second preset value. In the above example, the target models should be added in the order of express, special express, special car, carpooling and luxury car.

[0107] In some implementations of this embodiment, if there are two car models that are selected the same number of times, the two car models can be ranked according to the acceptance of other users, and the car model with a high acceptance can be added to the target car models first.

[0108] For example, in this embodiment, sub-step S1062, adding target models in the order of the number of times each model is selected from most to least, until the number of target models reaches a second preset value, may include:

[0109] In the order of the number of times the backup recommended models that are not the target model among the models are selected from most to least, the backup recommended models are added as the target models in sequence.

[0110] During the implementation process, if the car models recommended by the server 110 for the user to be responded to include express models and carpool models, and the number of target models at this time is 2, which is less than the second preset value (for example, 4), it is necessary to calculate the number of times the backup recommended car models other than the target model are selected in the historical orders of the user to be responded to, that is, the number of times other car models (which may be luxury car models, special car models or business car models, etc.) other than express models and carpool models are selected, and add the backup recommended car models as target models in order from most to least times until the number of target models reaches the second preset value.

[0111] In some embodiments, the types of vehicle models selected in all historical orders of the user to be responded to may only be express models and carpool models, and these two types of vehicle models are the same as the recommended target vehicle models, that is, there are no alternative recommended vehicle models. At this time, target vehicle models can be added according to the order of public acceptance of other vehicle models except express models and carpool models until the number of target vehicle models reaches a second preset value.

[0112] In another implementation of this embodiment, please refer to Figure 7 , Figure 7 The second sub-step flow chart of step S106 provided in the embodiment of the present application. In this embodiment, step S106 may include:

[0113] Sub-step S1063, calculating the number of times each car model is selected in the order information of other users within the preset time range of the service request time and within the preset distance range of the pick-up location.

[0114] Sub-step S1064, adding target models in the order of the number of times each model is selected from most to least, until the number of target models reaches a second preset value.

[0115] During the implementation process, when completing the target vehicle models, the server 110 can also obtain orders within the preset time range of the service request time of the user to be responded to and within the preset distance range of the boarding location. For example, if the passenger's service request time is the current moment and the boarding location is location A, the server 110 obtains the order information of other users within a period of time before the current moment (for example, 15 minutes) and near location A (for example, within 3 km of location A), calculates the number of times each vehicle model is selected in the order information of other users, and adds target vehicle models in order from most to least times until the number of target vehicle models reaches a second preset value.

[0116] For example, assuming that within 15 minutes from the current moment, the number of orders from other users whose boarding location is within 3km of location A is 50. Among these 50 order information, the number of times the express car model is selected is 40 times, the number of times the express car model is selected is 30 times, the number of times the private car model is selected is 15 times, the number of times the carpooling model is selected is 35 times, and the number of times the luxury car model is selected is 10 times.

[0117] The target models are added in the order of the number of times each model is selected from most to least until the number of target models reaches the second preset value. In the above example, the target models should be added in the order of express, carpool, express, private car and luxury car.

[0118] In some implementations of this embodiment, if there are two car models that are selected the same number of times, they can be sorted according to their public acceptance, and the car model with higher public acceptance can be added to the target car models first.

[0119] For example, in this embodiment, sub-step S1062, adding target models in the order of the number of times each model is selected from most to least, until the number of target models reaches a second preset value, may include:

[0120] In the order of the number of times the backup recommended models that are not the target model among the models are selected from most to least, the backup recommended models are added as the target models in sequence.

[0121] During the implementation process, if the car models recommended by the server 110 for the responding user include express models, carpool models and express models, and the number of target car models at this time is 3, which is less than the second preset value (for example, 4), it is necessary to calculate the number of times the backup recommended car models other than the target car model are selected in the order information of other users within the preset time range of the service request time (for example, within 15 minutes) and within the preset distance range of the boarding location A (for example, within 3km), that is, the number of times other car models (which may be luxury car models, special car models or business car models, etc.) other than express models are selected, and add the backup recommended car models as target car models in order from most to least times until the number of target car models reaches the second preset value.

[0122] In some embodiments, within a preset time range of the service request time (e.g., within 15 minutes), the car models selected in the order information of other users whose pick-up location is within a preset distance range of location A (e.g., within 3 km) may only be express models, carpooling models, and express models. These three car models are the same as the recommended target car models, that is, there are no backup recommended car models. At this time, target car models can be added according to the order of public acceptance of other car models except express models, carpooling models, and express models until the number of target car models reaches a second preset value.

[0123] To summarize, the embodiment of the present application provides a method for recommending car models for an online car-hailing service, by obtaining the boarding location and service request time selected by the user to be responded to, obtaining the number of queued orders before the service request time and within a preset distance range of the boarding location, and recommending a target car model in the reservation order of the user to be responded to according to the number of queued orders, so that the reservation order of the user to be responded to can be responded to by the target car model after it is released, thereby speeding up the response speed of the order of the user to be responded to and improving the user experience.

[0124] Based on the same inventive concept, an online car-hailing model recommendation device 200 corresponding to the online car-hailing model recommendation method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned online car-hailing model recommendation method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0125] Please refer to Figure 8 , Figure 8 This is a functional module diagram of an online car-hailing vehicle model recommendation device 200 provided in an embodiment of the present application, wherein the device includes: a first acquisition module 210, a second acquisition module 220, a reservation order generation module 230, and a vehicle model recommendation module 240.

[0126] The first acquisition module 210 is used to obtain the pick-up location and service request time selected by the user to be responded to;

[0127] The second acquisition module 220 is used to acquire the number of queued orders within a preset distance range of the pickup location before the service request time;

[0128] A reservation order generating module 230, for generating a reservation order according to the number of queued orders, the boarding location and the service request time;

[0129] The vehicle model recommendation module 240 is used to recommend a target vehicle model in the reservation order of the waiting user according to the number of the queued orders.

[0130] The embodiment of the present application obtains the boarding location and service request time selected by the user to be responded to, obtains the number of queued orders before the service request time and within a preset distance range of the boarding location, and recommends a target vehicle model in the reservation order of the user to be responded to according to the number of queued orders, so that the reservation order of the user to be responded to can be responded to by the target vehicle model after it is released, thereby speeding up the response speed of the order of the user to be responded to and improving the user experience.

[0131] Optionally, in this embodiment, the vehicle model recommendation module 240 includes:

[0132] A judging submodule, used for judging whether the number of queued orders is greater than a first preset value;

[0133] A first historical order acquisition submodule is used to acquire a first target order and a second target order from the historical orders of the user to be responded to when the number of the queued orders is greater than the first preset value. The first target order is the historical order that was recently published and the number of queued orders at the time of publication is greater than the first preset value, and the second target order is the historical order that was recently published by the user to be responded to;

[0134] The first target vehicle model recommendation submodule is used to recommend a corresponding target vehicle model in the reservation order of the user to be responded to according to the vehicle model selected by the user to be responded to when issuing the first target order and the second target order.

[0135] Optionally, in this embodiment, the vehicle model recommendation module 240 further includes:

[0136] A second historical order acquisition submodule, configured to acquire the second target order from the historical orders of the user to be responded to when the number of the queued orders is not greater than the first preset value;

[0137] The second target vehicle model recommendation submodule is used to recommend a corresponding target vehicle model in the reservation order of the user to be responded to according to the vehicle model selected by the user to be responded to when issuing the second target order.

[0138] Optionally, in this embodiment, the online car-hailing vehicle model recommendation device 200 further includes:

[0139] The quantity determination module 250 is used to determine whether the quantity of the recommended target vehicle models reaches a second preset value;

[0140] The vehicle type completion module 260 is used to complete the number of the target vehicle types to a second preset value when the second preset value is not reached.

[0141] Optionally, in this embodiment, the vehicle model completion module 260 is specifically used to:

[0142] Calculate the number of vehicle models selected by the responding user in historical orders within a preset time range; and add target vehicle models in descending order of the number of times each vehicle model is selected until the number of target vehicle models reaches a second preset value.

[0143] Optionally, in this embodiment, the vehicle model completion module 260 is used to add target vehicle models in the order of the number of times each vehicle model is selected from most to least through the following steps, until the number of target vehicle models reaches a second preset value:

[0144] In the order of the number of times the backup recommended models that are not the target model among the models are selected from most to least, the backup recommended models are added as the target models in sequence.

[0145] Optionally, in other implementations of this embodiment, the vehicle model completion module 260 is specifically used to:

[0146] Calculate the number of times each vehicle model is selected in order information of other users within a preset time range of the service request time and within a preset distance range of the pickup location;

[0147] The target models are added in the order of the number of times each model is selected from most to least, until the number of the target models reaches a second preset value.

[0148] Specifically, the vehicle model completion module 260 is used to add target vehicle models in the order of the number of times each vehicle model is selected from most to least through the following steps:

[0149] The backup recommended models that are not the target model are sequentially added as the target model in the order of the number of times the backup recommended models among the models are selected from most to least.

[0150] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0151] Please refer to Fig. 9 The embodiment of the present application also provides an electronic device 10. In this embodiment, the electronic device 10 can be understood as the server 110 in the aforementioned steps, which is used to implement the steps of the online car-hailing model recommendation method provided in the aforementioned embodiment.

[0152] like Fig. 9 , which is a schematic diagram of the structure of an electronic device 10 provided in an embodiment of the present application, includes: a processor 11, a memory 12, and a bus 13. The memory 12 stores machine-readable instructions executable by the processor 11 (for example, Figure 8 The execution instructions corresponding to the first acquisition module 210, the second acquisition module 220, the reservation order generation module 230 and the vehicle model recommendation module 240 in the online car-hailing vehicle model recommendation device 200, etc.), when the electronic device 10 is running, the processor 11 communicates with the memory 12 through the bus 13, and when the machine-readable instructions are executed by the processor 11, the steps of the online car-hailing vehicle model recommendation method provided in the above embodiment are executed.

[0153] An embodiment of the present application also provides a storage medium on which a computer program is stored. When the computer program is executed by the processor 11, the steps of the above-mentioned online car-hailing model recommendation method are executed.

[0154] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, it can execute the above-mentioned online car-hailing model recommendation method, so that after the reservation order of the user to be responded to is released, it can be responded to by different car models, thereby speeding up the response speed of the order of the user to be responded to and improving the user experience.

[0155] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0156] In addition, the functional units in the various embodiments of the present application can be integrated into one. In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application are only for the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application.

[0157] It should be understood that the operations of the flow chart may not be implemented in order, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flow chart, or may remove one or more operations from the flow chart under the guidance of the content of this application.

[0158] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0159] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor 11. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device 10 (which can be a personal computer, a server 110, or a network 120 device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0160] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for recommending online car-hailing models, characterized in that: include: Get the pick-up location and service request time selected by the user to be responded to; Obtaining the number of queued orders within a preset distance range of the pickup location before the service request time; generating a reservation order according to the number of queued orders, the pick-up location and the service request time; Determining whether the number of queued orders is greater than a first preset value; If the number of queued orders is greater than the first preset value, a first target order and a second target order are obtained from the historical orders of the user to be responded to, wherein the first target order is the historical order that was published most recently and the number of queued orders when published is greater than the first preset value, and the second target order is the historical order that was published most recently by the user to be responded to; recommending a corresponding target vehicle model in the reservation order of the user to be responded to according to the vehicle model selected by the user to be responded to when publishing the first target order and the second target order, wherein when the first target order and the second target order are different orders, the target vehicle model recommended in the reservation order is the vehicle model selected in the first target order published by the user to be responded to and the vehicle model selected by the user to be responded to when publishing the second target order; when the first target order and the second target order are the same order, the target vehicle model recommended in the reservation order is the vehicle model selected by the user to be responded to when publishing the second target order, and The target vehicle model is displayed as a default checked state on the reservation order page, and the remaining vehicle models are folded and displayed on the reservation order page.

2. The method according to claim 1, characterized in that: The method further comprises: If the number of the queued orders is not greater than the first preset value, obtaining the second target order from the historical orders of the user to be responded to; According to the car model selected by the user to be responded to when issuing the second target order, a corresponding target car model is recommended in the reservation order of the user to be responded to.

3. The method according to claim 1, characterized in that The method further comprises: Determining whether the number of recommended target vehicle models reaches a second preset value; If the second preset value is not reached, the number of the target vehicle models is supplemented to the second preset value.

4. The method according to claim 3, characterized in that The step of filling the number of the target vehicle models to the second preset value includes: Calculate the number of vehicle models selected by the user to be responded to in the historical orders within a preset time range; The target models are added in the order of the number of times each model is selected from most to least, until the number of the target models reaches a second preset value.

5. The method according to claim 3, characterized in that: The step of filling the number of the target vehicle models to the second preset value includes: Calculate the number of times each vehicle model is selected in order information of other users within a preset time range of the service request time and within a preset distance range of the pickup location; The target models are added in the order of the number of times each model is selected from most to least, until the number of the target models reaches a second preset value.

6. The method according to claim 4 or 5, characterized in that: According to the order of the number of times each model is selected from most to least, the target model is added, including: The backup recommended models that are not the target model are sequentially added as the target model in the order of the number of times the backup recommended models among the models are selected from most to least.

7. A device for recommending online car-hailing models, characterized in that: include: The first acquisition module is used to obtain the pick-up location and service request time selected by the user to be responded to; A second acquisition module is used to acquire the number of queued orders within a preset distance range of the pick-up location before the service request time; A reservation order generating module, used for generating a reservation order according to the number of queued orders, the boarding location and the service request time; A vehicle model recommendation module, used to determine whether the number of queued orders is greater than a first preset value; If the number of queued orders is greater than the first preset value, a first target order and a second target order are obtained from the historical orders of the user to be responded to, wherein the first target order is the historical order that was published most recently and the number of queued orders when published is greater than the first preset value, and the second target order is the historical order that was published most recently by the user to be responded to; recommending a corresponding target vehicle model in the reservation order of the user to be responded to according to the vehicle model selected by the user to be responded to when publishing the first target order and the second target order, wherein when the first target order and the second target order are different orders, the target vehicle model recommended in the reservation order is the vehicle model selected in the first target order published by the user to be responded to and the vehicle model selected by the user to be responded to when publishing the second target order; when the first target order and the second target order are the same order, the target vehicle model recommended in the reservation order is the vehicle model selected by the user to be responded to when publishing the second target order, and The target vehicle model is displayed as a default checked state on the reservation order page, and the remaining vehicle models are folded and displayed on the reservation order page.

8. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of any method as claimed in claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are executed.

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