CN111210036A

CN111210036A - Method and system for determining recommended boarding point

Info

Publication number: CN111210036A
Application number: CN201811392056.0A
Authority: CN
Inventors: 余鹏; 罗慕良; 陈欢; 宋奇; 马利
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2018-11-21
Filing date: 2018-11-21
Publication date: 2020-05-29
Anticipated expiration: 2038-11-21
Also published as: CN111210036B

Abstract

The application discloses a method and a system for determining recommended boarding points. The method for determining the recommended boarding point comprises the following steps: determining at least one candidate interest point according to the search keyword; determining at least one relevant pick-up point for the at least one candidate point of interest; and determining at least one related boarding point as a recommended boarding point. The method provided by the application can guide the user to select and recommend the boarding point, and the influence of the unreasonable boarding point on the driver pick-up efficiency and the user travel experience is avoided.

Description

Method and system for determining recommended boarding point

Technical Field

The application relates to the field of network taxi appointment, in particular to a method and a system for determining recommended boarding points.

Background

In recent years, with the rapid development of mobile communication technology, a great amount of application software based on intelligent terminals is emerging. Car-call applications are one of those that are popular with the general public. The passenger inputs the information of the starting place and the destination through the client and sends the vehicle using request. The driver takes over the drive according to the information of the starting place of the passenger. In practice, however, the origin of the passenger input may be inside a building, in a body of water, etc., where the driver cannot reach. If these places are used as boarding points, the drivers often cannot receive the passengers smoothly, which seriously affects the use feeling of both the drivers and the passengers. Therefore, it is desirable to provide a method and a system for prompting recommended boarding points, so as to guide passengers to select appropriate boarding points, and improve the pick-up efficiency of drivers and the travel experience of passengers.

Disclosure of Invention

The application provides a method and a system for determining recommended boarding points, which can provide the recommended boarding points for a user to select, reduce the probability of unreasonable boarding points selected by the user, and further improve the driving receiving efficiency of a driver and the traveling experience of the user.

A first aspect of the present application provides a method of determining a recommended pick-up point. The method for determining the recommended boarding point comprises the following steps: determining at least one candidate interest point according to the search keyword; determining at least one relevant pick-up point for the at least one candidate point of interest; and determining at least one related boarding point as a recommended boarding point.

In some embodiments, the determining at least one candidate point of interest according to the search keyword comprises: determining a related interest point list according to the search keyword; predicting the selected probability of each relevant interest point in the list; and determining the at least one candidate interest point according to the selected probability of each relevant interest point in the list.

In some embodiments, said determining at least one relevant pick-up point for said at least one candidate point of interest comprises: obtaining position information of the at least one candidate interest point; and determining at least one relevant boarding point for the candidate interest point at least according to the position information of the candidate interest point.

In some embodiments, the determining at least one relevant boarding point for the candidate point of interest based at least on the location information of the candidate point of interest comprises: determining at least one historical vehicle getting-on point within a certain range from the candidate interest point according to the position information of the candidate interest point; determining the at least one historical pick-up point as the at least one associated pick-up point.

In some embodiments, said determining at least one relevant pick-up point for said at least one candidate point of interest comprises: judging whether the at least one candidate interest point is a point region or a surface region; determining at least one relevant boarding point at least according to the judgment that the at least one candidate interest point is a point area or a surface area.

In some embodiments, said determining at least one relevant pick-up point based on a determination that said at least one candidate point of interest is a point region or a face region comprises: when the candidate interest point is the point area, determining the candidate interest point as a related boarding point; when the candidate point of interest is a face region, at least one relevant pick-up point located within the candidate point of interest range is determined.

In some embodiments, said determining at least one said associated pick-up point as a recommended pick-up point comprises: acquiring user position information; and screening out at least one recommended boarding point from at least one relevant boarding point according to the user position information.

In some embodiments, said determining at least one said associated pick-up point as a recommended pick-up point comprises: acquiring historical vehicle using information of a user; and screening out at least one recommended boarding point from at least one relevant boarding point according to the historical vehicle information of the user.

In some embodiments, said determining at least one said associated pick-up point as a recommended pick-up point comprises: acquiring the popularity of the related boarding points; and screening out at least one recommended boarding point from at least one relevant boarding point according to the popularity.

In some embodiments, said determining at least one said associated pick-up point as a recommended pick-up point further comprises: and screening the at least one recommended boarding point from the at least one related boarding point on the basis of the search keyword.

In some embodiments, said screening said at least one recommended pick-up point from at least one of said related pick-up points based on said search keyword comprises: determining text similarity between the search keyword and at least one related boarding point; and screening the at least one recommended boarding point from the at least one relevant boarding point according to the text similarity.

In some embodiments, the method for determining a recommended pick-up point further comprises: and sending the at least one recommended boarding point to a terminal, and enabling the at least one recommended boarding point to have a prompt mark for guiding a user to pay attention when the terminal displays the recommended boarding point.

A second aspect of the present application provides a system for determining a recommended pick-up point. The system for determining the recommended boarding point comprises a candidate interest point determining module, a related boarding point determining module and a recommended boarding point determining module; the candidate interest point determining module is used for determining at least one candidate interest point according to the search keyword; the relevant boarding point determining module is used for determining at least one relevant boarding point for the at least one candidate interest point; the recommended boarding point determining module is used for determining at least one related boarding point as a recommended boarding point.

In some embodiments, the candidate point of interest determination module further comprises: a related interest point determining unit, a selected probability predicting unit and a candidate interest point determining unit; the related interest point determining unit is used for determining a related interest point list according to the search keyword; the selected probability prediction unit is used for predicting the selected probability of each related interest point in the list; the candidate interest point determining unit is configured to determine the at least one candidate interest point according to the selected probability of each relevant interest point in the list.

In some embodiments, the relevant boarding point determination module further includes an interest point information acquisition unit and a relevant boarding point determination unit; the interest point information acquisition unit is used for acquiring the position information of the at least one candidate interest point; the related boarding point determining unit is used for determining at least one related boarding point for the candidate interest point at least according to the position information of the candidate interest point.

In some embodiments, the relevant pick-up point determination unit is further configured to: determining at least one historical vehicle getting-on point within a certain range from the candidate interest point according to the position information of the candidate interest point; determining the at least one historical pick-up point as the at least one associated pick-up point.

In some embodiments, the relevant pick-up point determining module further comprises an area judging unit and a relevant pick-up point determining unit; the region judging unit is used for judging whether the at least one candidate interest point is a point region or a surface region; the related boarding point determining unit is used for determining at least one related boarding point at least according to the judgment that the at least one candidate interest point is a point area or a surface area.

In some embodiments, the relevant pick-up point determination unit is further configured to: when the candidate interest point is the point area, determining the candidate interest point as a related boarding point; when the candidate point of interest is a face region, at least one relevant pick-up point located within the candidate point of interest range is determined.

In some embodiments, the recommended boarding point determining module further comprises a user location obtaining unit and a recommended boarding point determining unit; the user position acquisition unit is used for acquiring user position information; the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the user position information.

In some embodiments, the recommended boarding point determination module further comprises a history information acquisition unit and a recommended boarding point determination unit; the historical information acquisition unit is used for acquiring historical vehicle information of a user; the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the historical vehicle using information of the user.

In some embodiments, the recommended boarding point determining module further includes a boarding point information obtaining unit and a recommended boarding point determining unit; the getting-on point information acquisition unit is used for acquiring the popularity of the related getting-on points; the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the popularity.

In some embodiments, the recommended pick-up point determination module is further configured to: and screening the at least one recommended boarding point from the at least one related boarding point on the basis of the search keyword.

In some embodiments, the recommended boarding point determining module further comprises a text similarity determining unit and a recommended boarding point determining unit; the text similarity determining unit is used for determining the text similarity between the search keyword and at least one related boarding point; and the recommended boarding point determining unit is used for screening the at least one recommended boarding point from the at least one relevant boarding point according to the text similarity.

In some embodiments, the system for determining a recommended pick-up point further comprises a sending module, wherein the sending module is configured to: and sending the at least one recommended boarding point to a terminal, and enabling the at least one recommended boarding point to have a prompt mark for guiding a user to pay attention when the terminal displays the recommended boarding point.

A third aspect of the present application provides an apparatus for determining a recommended pick-up point. The apparatus for determining a recommended pick-up point comprises at least one storage medium and at least one processor, wherein the at least one storage medium is configured to store computer instructions; the at least one processor is configured to execute the computer instructions to implement a method of determining a recommended pick-up point.

A fourth aspect of the present application provides a computer-readable storage medium. The storage medium stores computer instructions that, when executed by a computer, implement a method of determining recommended pick-up points.

Drawings

The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a system for determining recommended pick-up points according to some embodiments of the present application;

FIG. 2 is a schematic diagram of an exemplary computing device shown in accordance with some embodiments of the present application;

FIG. 3 is a schematic diagram of exemplary hardware and/or software of a mobile device shown in accordance with some embodiments of the present application;

FIG. 4 is a block diagram of a system for determining recommended pick-up points according to some embodiments of the present application;

FIG. 5 is an exemplary flow chart of a method of determining recommended pick-up points according to some embodiments of the present application;

FIG. 6 is an exemplary flow diagram illustrating the determination of candidate points of interest according to some embodiments of the present application;

FIG. 7 is an exemplary flow chart illustrating the determination of relevant pick-up points according to some embodiments of the present application;

FIG. 8 is an exemplary flow chart illustrating the determination of relevant pick-up points according to some embodiments of the present application;

FIG. 9 is an exemplary flow chart illustrating the determination of recommended pick-up points from related pick-up points according to some embodiments of the present application;

FIG. 10 is an exemplary flow chart illustrating the determination of recommended pick-up points from related pick-up points according to some embodiments of the present application;

FIG. 11 is a schematic illustration of a display interface for prompting a recommended pick-up point according to some embodiments of the present application.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Although various references are made herein to certain modules or units in a system according to embodiments of the present application, any number of different modules or units may be used and run on a client and/or server. The modules are merely illustrative and different aspects of the systems and methods may use different modules.

Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The embodiment of the application can be applied to different traffic service systems, including but not limited to one or a combination of land, river, lake, sea, air and the like. For example, a human powered vehicle, a transportation means, an automobile (e.g., a small car, a bus, a large transportation vehicle, etc.), a rail transportation (e.g., a train, a motor car, a high-speed rail, a subway, etc.), a ship, an airplane, an aircraft, a hot air balloon, an unmanned vehicle, a transportation system to which management and/or distribution is applied, a delivery/reception express, etc., and the like. The application scenarios of the different embodiments of the present application include, but are not limited to, one or a combination of several of a web page, a browser plug-in, a client, a customization system, an intra-enterprise analysis system, an artificial intelligence robot, and the like. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these figures. For example, other similar guided user parking systems.

FIG. 1 is a schematic diagram of an application scenario of a system for determining recommended pick-up points according to some embodiments of the present application. The system for determining recommended boarding points 100 may determine and recommend recommended boarding points to passengers, and guide the passengers to select appropriate boarding points. The system 100 for determining recommended pick-up points may be an online service platform for internet services. For example, the system 100 for determining recommended pick-up points may be an online transportation service platform for a transportation service. In some embodiments, the system for determining recommended pick-up points 100 may be applied to network appointment services, such as taxi calls, express calls, special calls, mini-bus calls, carpools, bus services, driver employment and pickup services, and the like. In some embodiments, the system 100 for determining recommended pick-up points may also be applied to designated driving, express delivery, take-away, and the like. The system 100 for determining recommended pick-up points may be an online service platform including a server 110, a network 120, a user terminal 130, and a database 140. The server 110 may include a processing device 112.

In some embodiments, the server 110 may be used to process information and/or data related to determining recommended pick-up points. The server 110 may be a stand-alone server or a group of servers. The set of servers can be centralized or distributed (e.g., server 110 can be a distributed system). The server 110 may be regional or remote in some embodiments. For example, server 110 may access information and/or profiles stored in user terminal 130, database 140, through network 120. In some embodiments, the server 110 may be directly connected to the user terminal 130, the database 140 to access information and/or material stored therein. In some embodiments, the server 110 may execute on a cloud platform. For example, the cloud platform may include one or any combination of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like.

In some embodiments, the server 110 may include a processing device 112. The processing device 112 may process data and/or information related to the service request to perform one or more of the functions described herein. For example, the processing device 112 may receive the car use request signal transmitted by the user terminal 130 and provide the recommended boarding point to the user. In some embodiments, the processing device 112 may include one or more sub-processing devices (e.g., a single core processing device or a multi-core processing device). By way of example only, the processing device 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processor (GPU), a Physical Processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may facilitate the exchange of data and/or information. In some embodiments, one or more components of the system 100 (e.g., server 110, user terminal 130, database 140) that determine recommended pick-up points may send data and/or information to other components of the system 100 that determine recommended pick-up points via the network 120. In some embodiments, network 120 may be any type of wired or wireless network. For example, network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or Internet switching points 120-1, 120-2, …, through which one or more components of the system 100 that determine recommended pick-up points may connect to the network 120 to exchange data and/or information.

In some embodiments, the user may obtain the recommended pick-up point via the user terminal 130. In some embodiments, the user terminal 130 may include one or any combination of a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, an in-vehicle device 130-4, and the like. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the smart furniture device may include a smart lighting device, a control device for a smart appliance, a smart monitoring device, a smart television, a smart camera, an intercom, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, smart footwear, smart glasses, smart helmet, smart watch, smart clothing, smart backpack, smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may comprise a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a POS device, and the like, or any combination thereof. In some embodiments, the metaverse device and/or the augmented reality device may include metaverse helmets, metaverse glasses, metaverse eyewear, augmented reality helmets, augmented reality glasses, augmented reality eyewear, and the like, or any combination thereof. In some embodiments, user terminal 130 may include a location-enabled device to determine the location of the user and/or user terminal 130.

Database 140 may store data and/or instructions. In some embodiments, database 140 may store profiles obtained from user terminals 130. In some embodiments, database 140 may store information and/or instructions for server 110 to perform or use to perform the example methods described herein. In some embodiments, database 140 may include mass storage, removable storage, volatile read-write memory (e.g., random access memory RAM), read-only memory (ROM), the like, or any combination thereof. In some embodiments, database 140 may be implemented on a cloud platform. For example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, and the like, or any combination thereof.

In some embodiments, database 140 may be connected to network 120 to communicate with one or more components of system 100 (e.g., server 110, user terminal 130, etc.). One or more components of the system 100 that determine recommended pick-up points may access the materials or instructions stored in the database 140 via the network 120. In some embodiments, the database 140 may be directly connected to or in communication with one or more components (e.g., server 110, user terminal 130) in the system 100 that determine recommended pick-up points. In some embodiments, database 140 may be part of server 110.

FIG. 2 is a schematic diagram of an exemplary computing device according to some embodiments of the present application. In some embodiments, the server 110 and/or the requester terminal 130 may be implemented on a computing device 200. For example, the processing device 112 may implement and perform the functions of the processing device 112 disclosed herein on the computing device 200. As shown in fig. 2, computing device 200 may include a processor 220, a read only memory 230, a random access memory 240, a communication port 250, an input/output interface 260, and a hard disk 270.

The processor 220 can execute the computing instructions (program code) and perform the functions of the determine recommended pick-up system 100 described herein. The computing instructions may include programs, objects, components, data structures, procedures, modules, and functions (which refer to specific functions described herein). For example, the processor 220 may process image or text data obtained from any other component of the system 100 that determines a recommended pick-up point. In some embodiments, processor 220 may include microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), Central Processing Units (CPU), Graphics Processing Units (GPU), Physical Processing Units (PPU), microcontroller units, Digital Signal Processors (DSP), Field Programmable Gate Array (FPGA), Advanced RISC Machines (ARM), programmable logic devices, any circuit or processor capable of executing one or more functions, or the like, or any combination thereof. For illustration only, the computing device 200 in fig. 2 depicts only one processor, but it should be noted that the computing device 200 in the present application may also include multiple processors.

The memory (e.g., Read Only Memory (ROM)230, Random Access Memory (RAM)240, hard disk 270, etc.) of the computing device 200 may store data/information obtained from any other component of the system 100 that determines a recommended pick-up point. Exemplary ROMs may include Mask ROM (MROM), Programmable ROM (PROM), erasable programmable ROM (PEROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. Exemplary RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDR SDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance (Z-RAM), and the like.

The input/output interface 260 may be used to input or output signals, data, or information. In some embodiments, the input/output interface 260 may interface the user with the system 100 for determining recommended pick-up points. In some embodiments, input/output interface 260 may include an input device and an output device. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, and the like, or any combination thereof. Exemplary output devices may include a display device, speakers, printer, projector, etc., or any combination thereof. Exemplary display devices may include Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) based displays, flat panel displays, curved displays, television equipment, Cathode Ray Tubes (CRTs), and the like, or any combination thereof. The communication port 250 may be connected to a network for data communication. The connection may be a wired connection, a wireless connection, or a combination of both. The wired connection may include an electrical cable, an optical cable, or a telephone line, among others, or any combination thereof. The wireless connection may include bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobile networks (e.g., 3G, 4G, or 5G, etc.), and the like, or any combination thereof. In some embodiments, the communication port 250 may be a standardized port, such as RS232, RS485, and the like. In some embodiments, the communication port 250 may be a specially designed port.

FIG. 3 is a schematic diagram of exemplary hardware and/or software of a mobile device shown in accordance with some embodiments of the present application. As shown in fig. 3, the mobile device 300 may include a communication unit 310, a display unit 320, a Graphics Processor (GPU)330, a Central Processing Unit (CPU)340, an input/output unit 350, a memory 360, a storage unit 370, and the like. In some embodiments, operating system 361 (e.g., iOS, Android, Windows Phone, etc.) and application programs 362 may be loaded from storage unit 370 into memory 360 for execution by CPU 340. The applications 362 may include a browser or application for receiving text, images, audio or other relevant information from the system 100 for determining recommended pick-up points.

To implement the various modules, units and their functionality described in this application, a computing device or mobile device may serve as a hardware platform for one or more of the components described in this application. The hardware elements, operating systems, and programming languages of these computers or mobile devices are conventional in nature, and those skilled in the art will be familiar with these techniques and will be able to adapt these techniques to the system described herein for determining recommended points of boarding. A computer with user interface elements may be used to implement a Personal Computer (PC) or other type of workstation or terminal device, and if suitably programmed, may also act as a server.

FIG. 4 is a block diagram of a system for determining recommended pick-up points according to some embodiments of the present application. As shown in FIG. 4, a system (e.g., processing device 112) for determining recommended pick-up points may include a candidate point of interest determination module 410, a relevant pick-up point determination module 420, a recommended pick-up point determination module 430, and a transmission module 440.

The candidate point of interest determination module 410 may be used to determine candidate points of interest. As shown in fig. 4, the candidate interest point determining module 410 may further include a related interest point determining unit 412, a selected probability predicting unit 414, and a candidate interest point determining unit 416.

The related interest point determining unit 412 may determine a related interest point list according to the search keyword. In some embodiments, the list of related points of interest may include at least one related point of interest. A Point of Interest (POI) may be a location related to a search keyword. In some embodiments, the related interest point determining unit 412 may retrieve interest points having the same or similar name as the search keyword as related interest points based on a keyword matching technique and compose a related interest point list.

The selection probability prediction unit 414 may predict a selection probability of each relevant point of interest in the list of relevant points of interest. In some embodiments, the probability of selection (otherwise referred to as "click probability") may reflect the user's propensity to select various related points of interest (or pick-up points related to the related points of interest). In some embodiments, the hit probability prediction unit 414 may determine the hit probability based on the ranking score of each relevant point of interest in the list of relevant points of interest. In some embodiments, the hit probability prediction unit 414 may determine a hit probability based on historical orders of the user over a period of time. In some embodiments, the hit probability prediction unit 414 may use historical orders in a period of time as samples to train to obtain a click probability calculation model, and calculate the click probability of each relevant interest point through the click probability calculation model.

The candidate point of interest determination unit 416 may determine candidate points of interest from the list of related points of interest. In some embodiments, the candidate point of interest determination unit 416 may determine at least one candidate point of interest according to a selected probability of each relevant point of interest in the list of relevant points of interest. In some embodiments, the candidate interest point determination unit 416 may determine at least one candidate interest point by way of thresholding. In some embodiments, the candidate interest point determining unit 416 may determine, as the candidate interest points, the top N related interest points that are ranked the top (e.g., the most probable to be selected) in the related interest point list, where N is an integer greater than or equal to 1 (e.g., 1, 2, 3, 4, 5, etc.).

The associated pick-up point determination module 420 may be used to determine an associated pick-up point. As shown in fig. 4, the related boarding point determination module 420 may further include a point-of-interest information acquisition unit 422, an area judgment unit 424, and a related boarding point determination unit 426.

The interest point information obtaining unit 422 may be configured to obtain relevant information of the interest point. In some embodiments, the point of interest information obtaining unit 422 may obtain location information of at least one candidate point of interest. In some embodiments, the location information of the candidate point of interest may include at least latitude and longitude coordinates. In some embodiments, the point of interest information obtaining unit 422 may obtain the location information of the candidate point of interest in a variety of ways, such as querying or retrieving the location information of the candidate point of interest from a map database (e.g., database 140) and/or vehicle GPS track data (or GPS logs) in the taxi-taking platform.

The region determining unit 424 may be configured to determine a region type of the point of interest. In some embodiments, the region determining unit 424 may determine that the at least one candidate point of interest is a point region or a face region. In some embodiments, a face region may represent a region-like geographic entity in the map data. The point region may represent a geographical entity that is dotted in the map data. In some embodiments, the area determination unit 424 may determine that the candidate interest point is a point area or a face area according to the tag type. In some embodiments, the region determining unit 424 may also determine a point region or a surface region by the real area occupied by the candidate interest point. In some embodiments, the area determination unit 424 may also determine that the candidate interest point is a point area or a surface area through other factors, such as the perimeter of the candidate interest point area, the linear distance between the two farthest points in the area, the walking distance (or walking time) between the two farthest points in the area, and the like.

The associated pick-up point determination unit 426 may determine an associated pick-up point from a plurality of pick-up points (e.g., a set of pick-up points). In some embodiments, the relevant pick-up point determination unit 426 may determine at least one relevant pick-up point for the candidate point of interest from a plurality of pick-up points (e.g., a set of pick-up points) based at least on the location information of the candidate point of interest. In some embodiments, the relevant pick-up point determination unit 426 may determine at least one relevant pick-up point for the candidate point of interest from a plurality of pick-up points (e.g., a set of pick-up points) based at least on a determination that the at least one candidate point of interest is a point region or a face region.

The recommended pick-up point determination module 430 may determine a recommended pick-up point from the associated pick-up points. As shown in fig. 4, the recommended boarding point determination module 430 may include a user location acquisition unit 431, a history information acquisition unit 432, a boarding point information acquisition unit 433, a text similarity determination unit 434, and a recommended boarding point determination unit 435.

The user position acquisition unit 431 may acquire user position information. The user location information may reflect the current location of the user. In some embodiments, the user location information may include at least latitude and longitude information where the user is currently located. In some embodiments, the user location obtaining unit 431 may obtain the location information of the user terminal 130 according to a positioning technology, that is, the user location information.

The history information acquisition unit 432 may acquire the user history use information. The user historical usage information may include historical boarding points, historical destinations, historical disembarking points, historical departure times, user credits, user historical ratings, and the like, or any combination thereof. In some embodiments, the history information obtaining unit 432 may call the history order information in the database 140 by the user ID to obtain the user history vehicle usage information corresponding to the user ID. In some embodiments, the user historical occupancy information may be used to obtain user preference habits (e.g., occupancy points and characteristics thereof of the user habits) and further used to determine recommended occupancy points.

The pick-up point information obtaining unit 433 may be configured to obtain information related to a pick-up point. In some embodiments, the pick-up point information obtaining unit 433 may obtain the degree of awareness of the relevant pick-up points. The awareness of the relevant boarding points may include a search heat, an identification, and the like, or a combination thereof. In some embodiments, the popularity of the related pick-up point may be expressed as a score value, with higher popularity being higher and vice versa being lower. In some embodiments, the popularity of the associated pick-up point may be used to determine whether the associated pick-up point is easily perceived and further used to determine a recommended pick-up point.

The text similarity determination unit 434 may be configured to determine a text similarity between two or more texts. In some embodiments, the text similarity determination unit 434 may determine the text similarity of the search keyword with at least one of the related boarding points. In some embodiments, the text similarity determination unit 434 may pre-process the search keyword and determine the text similarity of the search keyword or the pre-processed search keyword with each of the related boarding points. In some embodiments, the text similarity determination unit 434 may calculate the similarity of the search keyword (or the preprocessed search keyword) to the relevant boarding point based on a text similarity algorithm.

The recommended pick-up point determination unit 435 may screen at least one recommended pick-up point from the at least one relevant pick-up point. In some embodiments, the recommended pick-up point determination unit 435 may determine at least one recommended pick-up point from the at least one relevant pick-up point based on at least one of the user location information, historical vehicle usage information, and a degree of awareness of the relevant pick-up points.

The sending module 440 may be used to send information and/or data to the user terminal 130. In some embodiments, the sending module 440 may send and display the determined recommended pick-up point to the user terminal 130.

It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the above described methods and systems can be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It should be noted that the above descriptions of the candidate item display and determination system and the modules thereof are only for convenience of description, and are not intended to limit the present application within the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, the candidate point of interest determination module 410, the relevant pick-up determination module 420, the recommended pick-up determination module 430, and the sending module 440 disclosed in fig. 4 may be different modules in a system, or may be a module that performs the functions of two or more of the above-described modules. For example, the candidate point of interest determination module 410, the related boarding point determination module 420, and the recommended boarding point determination module 430 may be three modules, or one module may have the functions of determining the candidate point of interest, the related boarding point, and the recommended boarding point. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present application.

FIG. 5 is an exemplary flow chart of a method of determining recommended pick-up points according to some embodiments of the present application. As shown in FIG. 5, the method 500 of determining a recommended pick-up point may include:

step 510, determining at least one candidate interest point according to the search keyword. In particular, this step 510 may be performed by the candidate point of interest determination module 410.

In some embodiments, the search keyword may be various types of words, letters, numbers, characters, etc., or combinations thereof, which are input by the user through the user terminal 130. When a user inputs a search keyword at the user terminal 130, the user terminal 130 may transmit the search keyword to the server 110 through the network 120, and the processing device 112 may receive the search keyword and process it. In some embodiments, the manner in which the user enters the search keyword may include, but is not limited to, any combination of one or more of typing input, handwriting input, selection input, voice input, scanning input, and the like. Specifically, the typing input may include english input, chinese input, and the like depending on the language. The selection input may include selecting a keyword from a selection list, and the like. The scan input may include a scan barcode input, a scan two-dimensional code input, a scan text input, a scan picture input, and the like. For example, the search keyword may be a Chinese character directly handwritten by the user. For another example, the search keyword may be a word or letter identified from a user scanned picture input. For another example, the search keyword may be a word or letter recognized from a voice input by the user.

In some embodiments, the search keywords may be used to search for locations of interest to the user. For example, when a user takes a car using the taxi-taking software, keywords related to a starting place, a boarding point, and/or a destination may be acquired by inputting search keywords. For example, when the user calls a car for another user, the search keyword related to the getting-on/off position of the other user can be input on the car calling platform. In some embodiments, the user-entered search keywords may include keywords related to place names, business circles, place attributes (e.g., hotels, malls, movie theaters, etc.), addresses, and the like. In some embodiments, determining at least one candidate point of interest according to the search keyword may also be applied to the fields of maps, navigation, and the like.

In some embodiments, the candidate point of interest determination module 410 determining at least one candidate point of interest from the search keyword may include: determining a related interest point list according to the search keywords, predicting the selection probability of each related interest point in the list, and determining at least one candidate interest point according to the selection probability of each related interest point in the list. For more details regarding the determination of candidate points of interest from search keywords, reference may be made to fig. 6 and its associated description.

At step 520, at least one relevant pick-up point is determined for the at least one candidate point of interest. In particular, this step 520 may be performed by the relevant pick-up point determination model 420.

In some embodiments, for example, when the candidate point of interest is a piece of area (e.g., university of Beijing), it may be difficult to know the exact location where the user wishes to start the trip through the candidate point of interest. Therefore, it is necessary to determine a boarding point (e.g., Nanmen of Beijing university) related to the candidate point of interest based on the candidate point of interest, so as to further clarify the boarding location of the user. In some embodiments, determining at least one relevant pick-up point for at least one candidate point of interest may include: obtaining position information of at least one candidate interest point; and determining at least one relevant boarding point for the candidate interest points at least according to the position information of the candidate interest points. In some embodiments, the relevant pick-up point determination model 420 may determine that at least one candidate point of interest is a point region or a face region; and determining at least one relevant boarding point at least according to the judgment that the at least one candidate interest point is a point area or a surface area. For more details regarding the determination of relevant pick-up points for candidate points of interest, reference may be made to fig. 7-8 and their associated description.

At least one relevant pick-up point is determined as a recommended pick-up point, step 530. In particular, this step 530 may be performed by the recommended pick-up point determination module 430.

In some embodiments, the recommended boarding point determination module 430 may obtain the user location information, the historical user information of the user, and/or the popularity of the related boarding points, and determine at least one recommended boarding point from the at least one related boarding point according to the obtained user location information, the historical user information of the user, and/or the popularity of the related boarding points. In some embodiments, the recommended boarding point determining module 430 may determine a text similarity between the search keyword and at least one of the related boarding points, and screen the at least one recommended boarding point from the at least one related boarding point according to the text similarity. For more details regarding determining a recommended pick-up point from the associated pick-up points, reference may be made to FIGS. 9-10 and their associated description.

In some embodiments, after the recommended pick-up point module 430 determines the recommended pick-up point, the sending module 440 may send the determined recommended pick-up point to the user terminal 130 and display it. In some embodiments, a prompt mark may also be added to the recommended pick-up point so that the user may be guided to focus when displayed on the interface of the user terminal 130. For more details regarding cue markers, reference may be made to fig. 11 and its accompanying description.

It should be noted that the above description related to the flow 500 is only for illustration and explanation, and does not limit the applicable scope of the present application. Various modifications and changes to flow 500 may occur to those skilled in the art upon review of the present application. However, such modifications and variations are intended to be within the scope of the present application. For example, in some embodiments, relevant pick-up points may be obtained directly from search keywords. As another example, the recommended pick-up point may be determined directly from the candidate points of interest. For another example, after the recommended boarding point is determined in step 530, a path planning step may be added to plan a recommended path from the current location of the user to the recommended boarding point.

FIG. 6 is an exemplary flow chart illustrating the determination of candidate points of interest according to some embodiments of the present application. As shown in fig. 6, determining a candidate point of interest 600 may include:

step 610, determining a related interest point list according to the search keyword. In particular, step 610 may be performed by the relevant point of interest determination unit 412.

In some embodiments, the list of related points of interest may include at least one related point of interest. A Point of Interest (POI) may be a location related to a search keyword. Specifically, the related points of interest may include names, categories (such as point areas or area areas), longitude and latitude, and other information. In some embodiments, the related interest point determining unit 412 may retrieve interest points having the same or similar name as the search keyword as related interest points based on a keyword matching technique. The point of interest information may be stored in a database 140. Specifically, the related interest point determining unit 412 may retrieve the interest points with higher text similarity as related interest points by calculating text similarities between the names of the interest points in the database 140 and the search keywords, and form a related interest point list. For example, the interest points n (e.g., 10, 15, 20, 30, etc.) before the text similarity may be selected as the related interest points. As another example, points of interest with text similarity greater than a certain threshold (e.g., 60%, 80%, etc.) may be selected as relevant points of interest. The calculation of the text similarity may adopt any feasible calculation method in the prior art. For example, the text similarity calculation may include calculating one or more arbitrary combinations of a Jaccard (Jaccard) similarity coefficient, a cosine similarity, a manhattan distance, a euclidean distance, a minuscule distance, an edit distance, and the like between the two. In some embodiments, other relevant factors, such as any combination of one or more of user location, user historical search records, and the like, may also be considered in determining the list of relevant points of interest based on the search keywords. For example, the relevant interest point determining unit 412 may consider only interest points within a certain distance from the user (or in the same city as the user, etc.) as relevant interest points.

In some alternative embodiments, the method of determining the list of relevant points of interest based on the search keywords may also be any suitable method known in the art. For example, the related point of interest list may be determined based on a prefix, a core word or phrase, etc. of the search keyword. As another example, the list of related points of interest may be determined based on speech, semantics, etc. of the search keywords. In some embodiments, before determining the relevant interest point list according to the search keyword, the search keyword may be further analyzed, rewritten, corrected, and the like.

And step 620, predicting the selected probability of each relevant interest point in the list. In particular, step 620 may be performed by the hit probability prediction unit 414.

In some embodiments, the probability of selection (otherwise referred to as "click probability") may reflect the user's propensity to select various related points of interest (or pick-up points related to the related points of interest). In some embodiments, the selection probability may be expressed as a probability that a point of interest (or a pick-up point associated with the point of interest) has been historically selected by a user (e.g., the user, or all users, or users within a specified area, etc.). In some embodiments, each relevant point of interest in the list of relevant points of interest determined in step 610 may have a corresponding ranking score that may reflect the probability of selection of each relevant point of interest, and thus, the probability of selection of each relevant point of interest may be determined based on the ranking score. In some embodiments, the selection probability may be determined based on historical orders of the user over a period of time. For example, the ratio of the total times of selecting a certain related interest point by the user to the total amount of orders in the period of time can be determined as the selection probability of the related interest point. In some embodiments, a click probability calculation model may be obtained by training historical orders in a period of time as samples, and the click probability of each relevant interest point is calculated through the click probability calculation model. For example, the following features in the historical order may be extracted: and training an initial model by using the extracted characteristics to obtain the click probability calculation model. In some alternative embodiments, the probability of selecting each relevant interest point in the list may also be predicted by other existing methods, which is not limited in this application.

Step 630, determining the at least one candidate point of interest according to the selected probability of each relevant point of interest in the list. In particular, step 630 may be performed by the candidate point of interest determination unit 416.

In some embodiments, the server 110 may determine at least one candidate point of interest by thresholding. In some embodiments, the threshold setting may be determined manually or automatically. In some embodiments, the set threshold may also be determined experimentally. In some embodiments, the set threshold may be 80%, 70%, 60%, 50%, 40%, etc. For example, when the threshold is set to 80%, the candidate interest point determining unit 416 may only use the relevant interest points in the relevant interest point list whose hit probability is greater than or equal to 80% as the candidate interest points. In some embodiments, the candidate interest point determining unit 416 may determine, as the candidate interest points, the top N related interest points that are ranked the top (e.g., the most probable to be selected) in the related interest point list, where N is an integer greater than or equal to 1 (e.g., 1, 2, 3, 4, 5, etc.).

FIG. 7 is an exemplary flow chart illustrating the determination of relevant pick-up points according to some embodiments of the present application. As shown in fig. 7, an exemplary process 700 for determining relevant pick-up points may include:

step 710, obtaining location information of at least one candidate point of interest. Specifically, step 710 may be performed by the point of interest information obtaining unit 422.

In some embodiments, the location information of the candidate point of interest may include at least latitude and longitude coordinates. In some embodiments, the location information of the candidate interest point may further include one or more of altitude, road in the road network where the candidate interest point is located, region in the road network where the candidate interest point is located, and the like. In some embodiments, obtaining location information for a candidate point of interest may be performed in a variety of ways, for example, the location information for the candidate point of interest may be queried or retrieved from a map database (e.g., database 140). As another example, the location information of the candidate points of interest may be queried from vehicle GPS trajectory data (or GPS logs) in the taxi-taking platform.

Step 720, determining at least one relevant boarding point for the candidate interest point at least according to the position information of the candidate interest point. In particular, step 720 may be performed by the relevant boarding point determination unit 426.

In some embodiments, the relevant boarding point determination unit 426 may select at least one relevant boarding point for the candidate point of interest from a plurality of boarding points (e.g., a set of boarding points) based on the location information of the candidate point of interest. The set of pick-up points may be a set of location points on a transport services platform (e.g., a taxi landing) where all passengers may pick-up or where a driver may wait for a passenger to pick-up. In some embodiments, the set of pick-up points may consist of the pick-up points in all historical order data on the taxi-taking platform. In some embodiments, the pick-up point set may include any other suitable location for pick-up points, such as a gate number one in a certain cell, a gate east in a certain hospital, a temporary stop on a road network, and so forth. In some embodiments, at least one pick-up point within a range of radii (e.g., 500m, 300m, 200m, 100m, etc.) may be determined as a relevant pick-up point centered on the location of the candidate point of interest (e.g., to determine the latitude and longitude coordinates of the candidate point of interest). In some embodiments, all of the boarding points within a certain radius may be considered relevant boarding points, centered on the location of the candidate point of interest.

In some embodiments, other factors may also be considered in determining the relevant pick-up point. For example, the related boarding points can also be determined (e.g., screened out) according to the similarity between the names of the boarding points and the candidate interest point names. For example, pick-up points whose names have a similarity greater than some set threshold (e.g., 50%, 60%, 70%, etc.) to the candidate point of interest name may be selected as relevant pick-up points. For another example, a fixed number (e.g., 3, 5, 8, etc.) of boarding points may be selected as the relevant boarding points according to the distance between the boarding points and the candidate interest points.

FIG. 8 is an exemplary flow chart illustrating the determination of relevant pick-up points according to some embodiments of the present application. As shown in FIG. 8, an exemplary process 800 for determining relevant pick-up points may include:

step 810, determining at least one candidate point of interest as a point region or a surface region. Specifically, step 810 may be performed by the area determination unit 424.

In some embodiments, the face region may represent a regional-like geographic entity in the map data, e.g., Beijing university/Baolixi, spring, etc. The dot areas may represent geographical entities that are dotted in the map data, e.g. north-university east/north-university south/baoxin-west, etc. In some embodiments, the candidate interest point may be retrieved from a map database (e.g., the database 140), a tag of the candidate interest point (or a tag carried in the candidate interest point information) is obtained, and it is determined whether the candidate interest point is a point region or a surface region according to the tag type. For example, the label includes point data and surface data, the candidate interest point labeled with the point data is determined as a point region, and the candidate interest point labeled with the surface data is determined as a surface region. In some embodiments, the point region or the surface region may also be determined by the real area occupied by the candidate interest point. For example, when the area of the region corresponding to the candidate interest point is larger than a certain set threshold (e.g., 100, 200, 500, 1000 square meters), the candidate interest point may be determined as a surface region; otherwise, the candidate point of interest may be considered as a point region. In some embodiments, the determination of the dot area or the face area may also be made by other factors. For example, the candidate interest point may be determined to be a point region or a surface region according to the perimeter of the candidate interest point region, the linear distance between the two farthest points in the region, the walking distance (or walking time) between the two farthest points in the region, and other factors.

Step 820, determining at least one relevant boarding point according to at least the judgment that the at least one candidate interest point is a point area or a surface area. In particular, step 820 may be performed by the relevant pick-up point determination unit 426.

In some embodiments, when a candidate point of interest is a point region, it may be determined that the candidate point of interest is a relevant pick-up point. In some embodiments, when the candidate point of interest is a face region, at least one pick-up point located within the candidate point of interest range may be determined to be a relevant pick-up point. In some embodiments, when the candidate point of interest is a face region, at least one boarding point within a certain distance from the candidate point of interest (e.g., a straight-line distance from any point in the face region does not exceed 20m, 30m, 50m, 100m, etc.) may also be determined as a related boarding point. See fig. 7 and its associated description for further details regarding determining relevant pick-up points for candidate points of interest.

FIG. 9 is an exemplary flow chart illustrating the determination of recommended pick-up points from related pick-up points according to some embodiments of the present application. Specifically, the process 900 of determining a recommended pick-up point from the relevant pick-up points may be performed by the recommended pick-up point determination module 430. As shown in fig. 9, the process 900 may include:

in step 910, the recommended boarding point determining module 430 may obtain the user location information, the user's historical vehicle usage information, and/or the popularity of the relevant boarding points.

In some embodiments, the user location acquisition unit 431 may acquire user location information. Specifically, the user location information may reflect the current location of the user. In some embodiments, the user location information may include at least latitude and longitude information where the user is currently located. In some embodiments, the user location information may also include administrative area information, business district information, street information, and the like, or any combination thereof. In some embodiments, the user terminal 130 may determine the user location through positioning techniques and send the user location information to the server 110. In some embodiments, the user location obtaining unit 431 may obtain the location information of the user terminal 130 according to a positioning technology, that is, the user location information. Specifically, the Positioning technology may include, but is not limited to, any combination of one or more of a Global Positioning System (GPS), a satellite Positioning technology, a beidou Positioning technology, and a near field Positioning (e.g., Wifi Positioning and bluetooth Positioning).

In some embodiments, the history information acquisition unit 432 may acquire the user history usage information. Specifically, the user historical usage information may include historical boarding points, historical destinations, historical disembarking points, historical departure times, user credits, user historical ratings, and the like, or any combination thereof. In some embodiments, the user historical occupancy information may include at least historical boarding point information. In some embodiments, the historical information obtaining unit 432 may obtain the user historical usage information from the database 140. For example, the processing device 112 may obtain and/or process user historical order information, including user historical vehicle usage information, and store it in the database 140. The information obtaining unit 432 may obtain a user ID, which may uniquely determine user identity information, and further call the historical order information in the database 140 by the user ID to obtain the historical vehicle usage information of the user corresponding to the user ID. In some embodiments, the user historical occupancy information may be used to obtain user preference habits (e.g., occupancy points and characteristics thereof of the user habits) and further used to determine recommended occupancy points.

In some embodiments, the pick-up point information obtaining unit 433 may obtain the degree of awareness of the relevant pick-up points. Specifically, the degree of awareness of the relevant boarding points may include a degree of search heat, a degree of recognition, or the like, or a combination thereof. For example. The search popularity may include the number of times (or frequency, probability, etc.) the user searches and/or selects the associated pick-up point, and the identification may include whether the associated pick-up point is a business turn, has an easily identifiable logo (e.g., a building, sculpture, memorial archway, etc.). The boarding point information acquisition unit 433 may acquire the popularity information of the relevant boarding point from the database 140. In some embodiments, the popularity of the related pick-up point may be expressed as a score value, with higher popularity being higher and vice versa being lower. The score value can be obtained according to historical search conditions, user evaluation conditions, staff evaluation and the like of related boarding points. In some embodiments, the popularity of the associated pick-up point may be used to determine whether the associated pick-up point is easily perceived and further used to determine a recommended pick-up point.

In some embodiments, the recommended pick-up point determination module 430 may also obtain other information related to the user and/or the associated pick-up points. For example, a dockable attribute for the associated pick-up point may be obtained. Specifically, the berthable attributes of the relevant pick-up points may include road conditions of roads on which the relevant pick-up points are located (e.g., one-way roads, two-way roads, congestion conditions, whether roadblocks exist, whether highway exits, etc.), the number of drivers near the relevant pick-up points, and the like. In some embodiments, the berthable attributes of the associated pick-up points may be used to calculate information such as user walking costs, driver pickup costs, and the like, and further used to determine recommended pick-up points.

And step 920, screening out at least one recommended boarding point from at least one relevant boarding point according to the position information of the user, the historical vehicle using information of the user and/or the popularity of the relevant boarding points. Specifically, step 920 may be performed by the recommended boarding point determining unit 435.

In some embodiments, the recommended pick-up point determination unit 435 may determine at least one recommended pick-up point from the at least one relevant pick-up point based on the user location information. For example, the recommended boarding point determining unit 435 may determine walking costs of the user for traveling to each relevant boarding point according to the user location information and the location information of the relevant boarding points, rank the relevant boarding points based on the walking costs, and select at least one (e.g., 1, 2, 3, 4, 5, etc.) relevant boarding point with the lowest walking cost as the recommended boarding point. For another example, the recommended boarding point determining unit 435 may further determine, as the recommended boarding point, at least one related boarding point closest to the linear distance according to the linear distance between the user and the related boarding point. For another example, the recommended boarding point may be determined according to and/or in combination with information such as walking time of the user to the relevant boarding point, the number of traffic lights, and the like.

In some embodiments, the recommended pick-up point determination unit 435 may determine at least one recommended pick-up point from the at least one relevant pick-up point based on the historical occupancy information of the user. For example, the recommended boarding point determination unit 435 may determine, according to the historical vehicle information of the user, at least one (e.g., 1, 2, 3, etc.) boarding point that is selected most by the user history in the relevant boarding points as the recommended boarding point. In some embodiments, the user historical vehicle usage information may be the user's own historical vehicle usage information. In some embodiments, the user historical in-car information may also include historical in-car information of other users associated with the user. For example, the associated other user may be a home address, a company address, or the like that is the same or similar to the user.

In some embodiments, the recommended pick-up point determination unit 435 may determine at least one recommended pick-up point from the at least one relevant pick-up point according to the popularity of the relevant pick-up points. For example, the recommended boarding point determining unit 435 may determine, according to the popularity information of the relevant boarding points, at least one (e.g., 1, 2, 3, etc.) relevant boarding point with the highest popularity as the recommended boarding point.

In some embodiments, the recommended pick-up point determination unit 435 may further determine at least one recommended pick-up point from the at least one relevant pick-up point by comprehensively considering both or all of the user location information, the user's historical pick-up information, and the awareness of the relevant pick-up points. For example, different weights may be set for the walking cost of the user, the historical selection times of the user, and the popularity of the related boarding points, a composite score may be calculated for each related boarding point, and at least one (e.g., 1, 2, 3, 4, 5, etc.) related boarding point with the highest composite score may be selected as the recommended boarding point.

FIG. 10 is an exemplary flow chart illustrating the determination of recommended pick-up points from related pick-up points according to some embodiments of the present application. Specifically, the process 1000 of determining recommended pick-up points from the relevant pick-up points may be performed by the recommended pick-up point determination module 430. As shown in FIG. 10, a process 1000 for determining a recommended pick-up may include:

step 1010, determining the text similarity between the search keyword and at least one of the related boarding points. Specifically, step 1010 may be performed by the text similarity determination unit 434.

In some embodiments, the text similarity determination unit 434 may pre-process the search keywords. For example, a search keyword (e.g., "Beijing university") may be rewritten as a synonym (e.g., "Beijing university"). For another example, the text similarity determination unit 434 may check and correct spelling errors in the search keywords, and obtain the preprocessed search keywords. For another example, the search keyword may be subjected to a word segmentation process (e.g., "beijing road KFC" processed to "beijing road/KFC"). Further, the text similarity determination unit 434 may determine the text similarity of the search keyword or the preprocessed search keyword with each of the related boarding points. In some embodiments, the similarity of the search keyword (or pre-processed search keyword) to the relevant pick-up point may be calculated based on a text similarity algorithm. Specifically, the text similarity algorithm may include a cosine similarity method, a simple common lexical method, an edit distance method, a SimHash algorithm, a Jaccard similarity coefficient method, a euclidean distance method, a manhattan distance method, or the like, or any combination thereof.

And step 1020, screening at least one recommended boarding point from the at least one relevant boarding point according to the text similarity. Specifically, step 1020 may be performed by recommended boarding point determination unit 435. In some embodiments, the recommended boarding point determination unit 435 may select at least one (e.g., 1, 2, 3, etc.) related boarding point with the highest ranking of text similarity as the recommended boarding point. In some embodiments, the recommended boarding point determination unit 435 may select at least one relevant boarding point with text similarity exceeding a set threshold (e.g., 50%, 70%, etc.) as the recommended boarding point.

FIG. 11 is a schematic illustration of a display interface for prompting a recommended pick-up point according to some embodiments of the present application. As shown in fig. 11, the display interface may include a search input area, a boarding point display area, and the like. In this embodiment, when the user inputs the departure place name 1102 in the search input area of the display interface, a plurality of related departure points related to the departure place name are displayed on the interface. Specifically, the interface displays the relevant pick-up point names 1108 (including 1108-1, 1108-2), pick-up point locations 1110 (including 1110-1, 1110-2), and the like. In response to the recommended points of approach from the relevant points of approach, the interface displays a prompt 1106 that guides the user to select or focus on in addition to the names 1104 of the recommended points of approach. The hinting marks that guide user selection or attention may be text hinting, font bolding, font highlighting, font underlining, and the like. For example, when the user inputs "beijing university" on the interface, the pick-up point display area on the interface displays a plurality of relevant pick-up points (e.g., beijing university east [ subway station ], chinese national department bank (cheng fu road branch) opposite, beijing university oral hospital, etc.) in a list manner in real time, and may display recommended pick-up points (e.g., beijing university-east 2 gate, beijing university-east a mouth northwest exit) among the relevant pick-up points in the front row of the list. In addition, the recommended boarding points can be marked with a word of 'recommended', so that the user can select the recommended boarding points more easily. The distance of each boarding point from the current position of the user can be displayed beside the boarding point so as to be convenient for the user to refer to. In some alternative embodiments, the cue indicia 1106 may be further elaborated. Specifically, the reason for the recommendation may be displayed next to the recommended pick-up point. For example, the probability of selection, the number of people selected, the degree of search heat, the walking distance, the walking time, and the like may be displayed beside the recommended boarding point to prompt and guide the user for selection.

The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) the probability that unreasonable boarding points are selected is reduced by providing recommended boarding points for passengers; (2) the trip experience of passengers is improved; (3) the driving receiving efficiency of the driver is improved, and if the long-term benefit of the driver is higher; (4) and the overall yield of the network appointment platform is improved. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code 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 latter scenario, the remote computer may be connected to the user's computer through any network format, such as 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), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of determining a recommended pick-up point, comprising:

determining at least one candidate interest point according to the search keyword;

determining at least one relevant pick-up point for the at least one candidate point of interest;

and determining at least one related boarding point as a recommended boarding point.

2. The method of determining recommended boarding points of claim 1, wherein said determining at least one candidate point of interest based on search keywords comprises:

determining a related interest point list according to the search keyword;

predicting the selected probability of each relevant interest point in the list;

and determining the at least one candidate interest point according to the selected probability of each relevant interest point in the list.

3. The method of determining a recommended pick-up point of claim 1,

said determining at least one relevant pick-up point for said at least one candidate point of interest comprises:

obtaining position information of the at least one candidate interest point;

and determining at least one relevant boarding point for the candidate interest point at least according to the position information of the candidate interest point.

4. The method of claim 3, wherein determining at least one relevant pick-up point for the candidate point of interest based at least on the location information of the candidate point of interest comprises:

determining at least one historical vehicle getting-on point within a certain range from the candidate interest point according to the position information of the candidate interest point;

determining the at least one historical pick-up point as the at least one associated pick-up point.

5. The method of determining recommended boarding points of claim 1, wherein said determining at least one relevant boarding point for said at least one candidate point of interest comprises:

judging whether the at least one candidate interest point is a point region or a surface region;

determining at least one relevant boarding point at least according to the judgment that the at least one candidate interest point is a point area or a surface area.

6. The method of claim 5, wherein determining at least one relevant pick-up point based on a determination that the at least one candidate point of interest is a point region or a face region comprises:

when the candidate interest point is the point area, determining the candidate interest point as a related boarding point;

when the candidate point of interest is a face region, at least one relevant pick-up point located within the candidate point of interest range is determined.

7. The method of determining a recommended pick-up point according to claim 1, wherein said determining at least one of said associated pick-up points as a recommended pick-up point comprises:

acquiring user position information;

and screening out at least one recommended boarding point from at least one relevant boarding point according to the user position information.

8. The method of determining a recommended pick-up point according to claim 1, wherein said determining at least one of said associated pick-up points as a recommended pick-up point comprises:

acquiring historical vehicle using information of a user;

and screening out at least one recommended boarding point from at least one relevant boarding point according to the historical vehicle information of the user.

9. The method of determining a recommended pick-up point according to claim 1, wherein said determining at least one of said associated pick-up points as a recommended pick-up point comprises:

acquiring the popularity of the related boarding points;

and screening out at least one recommended boarding point from at least one relevant boarding point according to the popularity.

10. The method of determining a recommended pick-up point according to claim 1, wherein said determining at least one of said associated pick-up points as a recommended pick-up point further comprises:

and screening the at least one recommended boarding point from the at least one related boarding point on the basis of the search keyword.

11. The method of determining recommended pick-up points of claim 10, wherein said screening said at least one recommended pick-up point from at least one of said related pick-up points based on said search keyword comprises:

determining text similarity between the search keyword and at least one related boarding point;

and screening the at least one recommended boarding point from the at least one relevant boarding point according to the text similarity.

12. The method of determining a recommended pick-up point as claimed in claim 1, further comprising:

and sending the at least one recommended boarding point to a terminal, and enabling the at least one recommended boarding point to have a prompt mark for guiding a user to pay attention when the terminal displays the recommended boarding point.

13. A system for determining recommended boarding points is characterized by comprising a candidate interest point determining module, a related boarding point determining module and a recommended boarding point determining module; wherein,

the candidate interest point determining module is used for determining at least one candidate interest point according to the search keyword;

the relevant boarding point determining module is used for determining at least one relevant boarding point for the at least one candidate interest point;

the recommended boarding point determining module is used for determining at least one related boarding point as a recommended boarding point.

14. The system for determining recommended boarding points of claim 13, wherein the candidate point of interest determination module further comprises: a related interest point determining unit, a selected probability predicting unit and a candidate interest point determining unit; wherein,

the related interest point determining unit is used for determining a related interest point list according to the search keyword;

the selected probability prediction unit is used for predicting the selected probability of each related interest point in the list;

the candidate interest point determining unit is configured to determine the at least one candidate interest point according to the selected probability of each relevant interest point in the list.

15. The system for determining recommended boarding points of claim 13, wherein the relevant boarding point determination module further comprises a point-of-interest information acquisition unit and a relevant boarding point determination unit; wherein,

the interest point information acquisition unit is used for acquiring the position information of the at least one candidate interest point;

the related boarding point determining unit is used for determining at least one related boarding point for the candidate interest point at least according to the position information of the candidate interest point.

16. The method of determining recommended pick-up points of claim 15, wherein the associated pick-up point determination unit is further configured to:

17. The system for determining recommended boarding points of claim 13, wherein the relevant boarding point determination module further comprises an area judgment unit and a relevant boarding point determination unit; wherein,

the region judging unit is used for judging whether the at least one candidate interest point is a point region or a surface region;

the related boarding point determining unit is used for determining at least one related boarding point at least according to the judgment that the at least one candidate interest point is a point area or a surface area.

18. The system for determining recommended pick-up points of claim 17, wherein the associated pick-up point determination unit is further configured to:

19. The system for determining recommended boarding points of claim 13, wherein the recommended boarding point determination module further comprises a user location acquisition unit and a recommended boarding point determination unit; wherein,

the user position acquisition unit is used for acquiring user position information;

the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the user position information.

20. The system for determining recommended boarding points of claim 13, wherein the recommended boarding point determination module further comprises a historical information acquisition unit and a recommended boarding point determination unit; wherein,

the historical information acquisition unit is used for acquiring historical vehicle using information of a user;

the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the historical vehicle using information of the user.

21. The system for determining recommended boarding points of claim 13, wherein the recommended boarding point determination module further comprises a boarding point information acquisition unit and a recommended boarding point determination unit; wherein,

the getting-on point information acquisition unit is used for acquiring the popularity of the related getting-on points;

the recommended boarding point determining unit is used for screening out at least one recommended boarding point from at least one relevant boarding point according to the popularity.

22. The system for determining a recommended pick-up point of claim 13, wherein the recommended pick-up point determination module is further configured to:

23. The system for determining recommended boarding points of claim 22, wherein the recommended boarding point determination module further comprises a text similarity determination unit and a recommended boarding point determination unit; wherein,

the text similarity determining unit is used for determining the text similarity between the search keyword and at least one related boarding point;

and the recommended boarding point determining unit is used for screening the at least one recommended boarding point from the at least one relevant boarding point according to the text similarity.

24. The system for determining recommended pick-up points of claim 13, further comprising a transmitting module for:

25. An apparatus for determining a recommended pick-up point, comprising at least one storage medium and at least one processor,

the at least one storage medium is configured to store computer instructions;

the at least one processor is configured to execute the computer instructions to implement the method of determining a recommended pick-up point as claimed in any one of claims 1-12.

26. A computer readable storage medium storing computer instructions which, when executed by a computer, implement a method of determining recommended pick-up points as claimed in any one of claims 1 to 12.