CN111210036B

CN111210036B - Method and system for determining recommended get-on point

Info

Publication number: CN111210036B
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: 2023-11-07
Anticipated expiration: 2038-11-21
Also published as: CN111210036A

Abstract

The application discloses a method and a system for determining recommended get-on points. The method for determining the recommended get-on point comprises the following steps: determining at least one candidate interest point according to the search keywords; determining at least one relevant get-on point for the at least one candidate point of interest; and determining at least one relevant get-on point as a recommended get-on point. The method provided by the application can guide the user to select the recommended get-on point, and avoid the unreasonable get-on point from influencing the driver receiving and driving efficiency and the travel experience of the user.

Description

Method and system for determining recommended get-on point

Technical Field

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

Background

In recent years, with the rapid development of mobile communication technology, a large number of application software based on intelligent terminals are emerging. Vehicle applications are one of the most popular types. The passenger inputs the information of the starting place and the destination through the client and sends a vehicle using request. The driver takes the drive ahead according to the information of the starting place of the passenger. In practice, however, the origin of the passenger input may be a location within the building, in water, etc., where the driver is not available. If these sites are used as boarding points, the driver often cannot smoothly get to the passengers, which can seriously affect the use experience of both the driver and the passengers. Therefore, it is desirable to provide a method and a system for prompting to recommend a get-on point, so as to guide passengers to select a proper get-on point, and improve the receiving and driving efficiency of drivers and the traveling experience of passengers.

Disclosure of Invention

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

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

In some embodiments, the determining at least one candidate point of interest from the search keyword comprises: determining a related interest point list according to the search keywords; 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, the determining at least one relevant entry point for the at least one candidate point of interest comprises: acquiring the position information of the at least one candidate interest point; and determining at least one relevant get-on 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 entry point for the candidate point of interest based at least on the location information of the candidate point of interest includes: determining at least one historical driving point within a certain range from the candidate interest point according to the position information of the candidate interest point; the at least one historical entry point is determined as the at least one relevant entry point.

In some embodiments, the determining at least one relevant entry point for the at least one candidate point of interest comprises: judging that the at least one candidate interest point is a point area or a surface area; and determining at least one relevant get-on 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 determining at least one relevant entry point according to the determination that the at least one candidate point of interest is a point region or a surface region includes: when the candidate interest points are the point areas, determining that the candidate interest points are relevant get-on points; when the candidate interest point is a face region, at least one relevant get-on point located within the candidate interest point is determined.

In some embodiments, the determining at least one of the relevant get-on points as a recommended get-on point comprises: acquiring user position information; and screening at least one recommended get-on point from at least one relevant get-on point according to the user position information.

In some embodiments, the determining at least one of the relevant get-on points as a recommended get-on point comprises: acquiring historical vehicle information of a user; and screening at least one recommended get-on point from at least one relevant get-on point according to the historical get-on information of the user.

In some embodiments, the determining at least one of the relevant get-on points as a recommended get-on point comprises: acquiring the awareness degree of the related boarding points; and screening at least one recommended get-on point from at least one relevant get-on point according to the awareness.

In some embodiments, the determining at least one of the relevant get-on points as a recommended get-on point further comprises: and screening at least one recommended get-on point from at least one relevant get-on point based on the search keyword.

In some embodiments, the selecting the at least one recommended entry point from at least one of the relevant entry points based on the search keyword includes: determining the text similarity between the search keyword and at least one relevant get-on point; and screening at least one recommended get-on point from at least one relevant get-on point according to the text similarity.

In some embodiments, the method for determining a recommended get-on point further includes: and sending the at least one recommended get-on point to a terminal, and enabling the at least one recommended get-on point to have a prompt mark for guiding the attention of a user when the terminal is displayed.

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

In some embodiments, the candidate point of interest determination module further comprises: the device 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 keywords; 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 used for determining the at least one candidate interest point according to the selected probability of each relevant interest point in the list.

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

In some embodiments, the related entry point determining unit is further configured to: determining at least one historical driving point within a certain range from the candidate interest point according to the position information of the candidate interest point; the at least one historical entry point is determined as the at least one relevant entry point.

In some embodiments, the related get-on point determining module further includes a region judging unit and a related get-on 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 get-on point determining unit is configured to determine at least one related get-on point at least according to a determination that the at least one candidate interest point is a point area or a surface area.

In some embodiments, the related entry point determining unit is further configured to: when the candidate interest points are the point areas, determining that the candidate interest points are relevant get-on points; when the candidate interest point is a face region, at least one relevant get-on point located within the candidate interest point is determined.

In some embodiments, the recommended get-on point determining module further includes a user position obtaining unit and a recommended get-on point determining unit; the user position acquisition unit is used for acquiring user position information; the recommended getting-on point determining unit is used for screening at least one recommended getting-on point from at least one relevant getting-on point according to the user position information.

In some embodiments, the recommended get-on point determining module further includes a history information acquiring unit and a recommended get-on point determining unit; the history information acquisition unit is used for acquiring history vehicle information of a user; the recommended boarding point determining unit is used for screening at least one recommended boarding point from at least one related boarding point according to the historical boarding information of the user.

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

In some embodiments, the recommended get-on point determination module is further to: and screening at least one recommended get-on point from at least one relevant get-on point based on the search keyword.

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

In some embodiments, the system for determining a recommended get-on point further includes a sending module, where the sending module is configured to: and sending the at least one recommended get-on point to a terminal, and enabling the at least one recommended get-on point to have a prompt mark for guiding the attention of a user when the terminal is displayed.

A third aspect of the present application provides an apparatus for determining a recommended get-on point. The device for determining the recommended get-on point comprises at least one storage medium and at least one processor, wherein the at least one storage medium is used for storing 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 a recommended pick-up point.

Drawings

The application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

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

FIG. 2 is a schematic diagram of an exemplary computing device, shown, according to some embodiments of the 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 application;

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

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

FIG. 6 is an exemplary flow chart for determining candidate points of interest according to some embodiments of the application;

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

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

FIG. 9 is an exemplary flow chart for determining recommended pick-up points from among related pick-up points according to some embodiments of the application;

FIG. 10 is an exemplary flow chart for determining recommended pick-up points from among related pick-up points according to some embodiments of the application;

FIG. 11 is a schematic diagram of a display interface for prompting a recommended get-on point according to some embodiments of the application.

Detailed Description

In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present application, and it is apparent to those of ordinary skill in the art that the present application may be applied to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in the specification and in the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

Although the present application makes various references 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 clients and/or servers. The modules are merely illustrative, and different aspects of the systems and methods may use different modules.

A flowchart is used in the present application to describe the operations performed by a system according to embodiments of the present application. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

Embodiments of the present application may be applied to different traffic service systems including, but not limited to, one or a combination of several of land, river, lake, sea, aviation, etc. For example, manpowered vehicles, mobility aids, automobiles (e.g., small vehicles, buses, large transportation vehicles, etc.), rail traffic (e.g., trains, motor cars, high-speed rails, subways, etc.), ships, airplanes, aircraft, hot air balloons, unmanned vehicles, delivery/express delivery, etc., employ management and/or distribution transportation systems, etc. The application scenarios of the different embodiments of the present application include, but are not limited to, one or a combination of several of web pages, browser plug-ins, clients, customization systems, in-enterprise analysis systems, artificial intelligence robots, and the like. It should be understood that the application scenario of the system and method of the present application is merely some examples or embodiments of the present application, and it is possible for those skilled in the art to apply the present application to other similar scenarios according to these drawings without the need for inventive labor. For example, other similar guidance users park systems.

Fig. 1 is a schematic view of an application scenario of a system for determining a recommended get-on point according to some embodiments of the application. The system 100 for determining recommended pick-up points may determine recommended pick-up points and recommend to the passenger, guiding the passenger to select an appropriate pick-up point. 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 on-line transportation service platform for transportation services. In some embodiments, the system 100 for determining recommended pick-up points may be applied to network taxi service such as taxi calls, express calls, private car calls, bus calls, carpool, bus service, driver employment and pick-up service, and the like. In some embodiments, the system 100 for determining recommended pick-up points may also be applied to services such as driving, express, take-away, and the like. The system 100 for determining recommended pick-up points may be an online service platform comprising 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, server 110 may be configured 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 server farm may be centralized or distributed (e.g., server 110 may be a distributed system). The server 110 may be regional or remote in some embodiments. For example, server 110 may access information and/or material stored in user terminal 130, database 140, via network 120. In some embodiments, the server 110 may be directly connected to the user terminal 130, database 140 to access information and/or material stored therein. In some embodiments, server 110 may execute on a cloud platform. For example, the cloud platform may include one of a private cloud, a public cloud, a hybrid cloud, a community cloud, a decentralized cloud, an internal cloud, or the like, or any combination thereof.

In some embodiments, 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 in the present application. For example, the processing device 112 may receive a vehicle use request signal sent by the user terminal 130 to provide a recommended get-on 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), an editable logic circuit (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like, or any combination thereof.

The network 120 may facilitate the exchange of data and/or information. In some embodiments, one or more components (e.g., server 110, user terminal 130, database 140) in system 100 that determine the recommended get-on point may send data and/or information over network 120 to other components in system 100 that determine the recommended get-on point. In some embodiments, network 120 may be any type of wired or wireless network. For example, the network 120 may include a cable network, a wired network, a fiber optic network, a telecommunications network, an internal network, 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, and 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 station and/or Internet switching points 120-1, 120-2, …, through which one or more components of the system 100 that determine recommended get-on points may connect to the network 120 to exchange data and/or information.

In some embodiments, the user may obtain a 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 130-2, a notebook 130-3, a vehicle-mounted 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 metaverse device, an augmented reality device, or 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 wristband, smart footwear, smart glasses, smart helmets, smart watches, smart clothing, smart back bags, smart accessories, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smart phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a POS device, etc., or any combination thereof. In some embodiments, the metaverse device and/or augmented reality device may include a metaverse helmet, metaverse glasses, metaverse eyepieces, augmented reality helmets, augmented reality glasses, augmented reality eyepieces, and the like, or any combination thereof. In some embodiments, the user terminal 130 may include a device with positioning functionality to determine the location of the user and/or the user terminal 130.

Database 140 may store materials and/or instructions. In some embodiments, database 140 may store material obtained from user terminal 130. In some embodiments, database 140 may store information and/or instructions for execution or use by server 110 to perform the exemplary methods described in this disclosure. 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), and 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 system 100 that determine recommended pick-up points may access materials or instructions stored in database 140 via network 120. In some embodiments, database 140 may be directly connected to or in communication with one or more components (e.g., server 110, user terminal 130) in 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 application. In some embodiments, server 110 and/or requester terminal 130 may be implemented on computing device 200. For example, the processing device 112 may be implemented on the computing device 200 and perform the functions of the processing device 112 as disclosed herein. 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 may execute the computing instructions (program code) and perform the functions of the determine recommended point-of-approach system 100 described herein. The computing instructions may include programs, objects, components, data structures, procedures, modules, and functions (which refer to particular functions described in this disclosure). For example, the processor 220 may process image or text data acquired from any other component of the determined recommended get-on point system 100. 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 Arrays (FPGA), advanced RISC Machines (ARM), programmable logic devices, any circuit and processor capable of executing one or more functions, and the like, or any combination thereof. For illustration only, computing device 200 in FIG. 2 depicts one processor, but it should be noted that computing device 200 in the present application may also include multiple processors.

The memory of computing device 200 (e.g., read Only Memory (ROM) 230, random Access Memory (RAM) 240, hard disk 270, etc.) may store data/information retrieved from any other component of the determine recommended point-of-entry system 100. Exemplary ROMs may include Mask ROM (MROM), programmable ROM (PROM), erasable programmable ROM (PEROM), electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), and digital versatile disk ROM, among others. Exemplary RAM may include Dynamic RAM (DRAM), double 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 enable a user to contact the system 100 that determines recommended pick-up points. In some embodiments, the 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 means 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, optical cable, or telephone line, or the like, or any combination thereof. The wireless connection may include bluetooth, wi-Fi, wiMax, WLAN, zigBee, a mobile network (e.g., 3G, 4G, 5G, etc.), etc., or any combination thereof. In some embodiments, the communication port 250 may be a standardized port, such as RS232, RS485, or the like. In some embodiments, 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 according to some embodiments of the 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, an operating system 361 (e.g., iOS, android, windows Phone, etc.) and application programs 362 may be loaded from the storage unit 370 into the memory 360 for execution by the CPU 340. The application 362 may include a browser or application for receiving text, images, audio, or other related information from the system 100 that determines recommended pick-up points.

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

FIG. 4 is a block diagram of a system for determining recommended pick-up points according to some embodiments of the 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, an associated 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 point of interest determination module 410 may further include a related point of interest determination unit 412, a selected probability prediction unit 414, and a candidate point of interest determination unit 416.

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

The selected probability prediction unit 414 may predict the selected probability of each relevant point of interest in the list of relevant points of interest. In some embodiments, the selection probability (or "click probability") may reflect a tendency of the user to select each relevant point of interest (or the on-board point related to the relevant point of interest). In some embodiments, the selected probability prediction unit 414 may determine the selected probability based on a ranking score for each relevant point of interest in the list of relevant points of interest. In some embodiments, the choice probability prediction unit 414 may determine the choice probability from a historical order of the user over a period of time. In some embodiments, the selected probability prediction unit 414 may train a click probability calculation model using the historical orders over a period of time as samples, and calculate the click probability of each relevant interest point using the click probability calculation model.

The candidate point of interest determination unit 416 may determine candidate points of interest from a 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 based on the selected probabilities of the respective related points of interest in 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 by way of thresholding. In some embodiments, candidate point of interest determination unit 416 may determine the top N top-ranked (e.g., the highest probability of being selected) related points of interest in the related point of interest list as candidate points of interest, where N is an integer greater than or equal to 1 (e.g., 1, 2, 3, 4, 5, etc.).

The relevant get on point determination module 420 may be used to determine the relevant get on point. As shown in fig. 4, the related getting-on point determining module 420 may further include a point-of-interest information acquiring unit 422, a region judging unit 424, and a related getting-on point determining unit 426.

The interest point information acquiring unit 422 may be configured to acquire related information of an interest point. In some embodiments, the point of interest information acquisition unit 422 may acquire 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 acquisition unit 422 may acquire 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 log) in a taxi-taking platform.

The region judgment unit 424 may be configured to judge the region type of the point of interest. In some embodiments, the region determination unit 424 may determine that at least one candidate point of interest is a point region or a face region. In some embodiments, the area of the face may represent a regional geographic entity in the map data. The point region may represent a geographical entity that is punctiform in the map data. In some embodiments, the region determination unit 424 may determine that the candidate point of interest is a point region or a face region according to the tag type. In some embodiments, the region determining unit 424 may also determine the point region or the area region by the real area occupied by the candidate interest point. In some embodiments, the region determining unit 424 may also determine that the candidate point of interest is a point region or a plane region by other factors, such as the perimeter of the candidate point of interest region, the straight line distance of the farthest two points in the region, the walking distance (or walking time) of the farthest two points in the region, and so on.

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

The recommended get-on point determination module 430 may determine a recommended get-on point from the relevant get-on points. As shown in fig. 4, the recommended getting-on point determination module 430 may include a user position acquisition unit 431, a history information acquisition unit 432, a getting-on point information acquisition unit 433, a text similarity determination unit 434, and a recommended getting-on 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 at which 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 the positioning technology, that is, the user location information.

The history information acquisition unit 432 may acquire user history car information. The user historical vehicle information may include historical departure points, historical destinations, historical departure points, historical departure times, user credits, user historical evaluations, and the like, or any combination thereof. In some embodiments, the history information acquisition unit 432 may call the history order information in the database 140 by the user ID to acquire the user history car information corresponding to the user ID. In some embodiments, the user historical occupancy information may be used to obtain user preference habits (e.g., the occupancy points and features thereof for which the user is accustomed) and further to determine recommended occupancy points.

The get-on point information acquiring unit 433 may be used to acquire information related to a get-on point. In some embodiments, the get-on point information acquiring unit 433 may acquire the awareness of the relevant get-on point. The awareness of the point of entry may include search heat, identification, etc., or a combination thereof. In some embodiments, the awareness of the relevant pick-up points may be expressed as a score value, with the higher the score value, the higher the awareness and vice versa. In some embodiments, the known degree of the relevant pick-up point may be used to determine whether the relevant pick-up point is easily perceived and further used to determine the recommended pick-up point.

The text similarity determination unit 434 may be used to determine 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 to at least one of the related getting-on 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 to each relevant entry point. In some embodiments, the text similarity determination unit 434 may calculate the similarity of the search keyword (or the preprocessed search keyword) and the related on-boarding point based on a text similarity algorithm.

The recommended getting-on point determination unit 435 may screen out at least one recommended getting-on point from at least one relevant getting-on point. In some embodiments, the recommended getting-on point determining unit 435 may determine at least one recommended getting-on point from at least one relevant getting-on point according to at least one of user location information, historical getting-on information, and awareness of relevant getting-on points.

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

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

It should be noted that the above description of the candidate display, determination system, and modules thereof is for descriptive convenience only and is not intended to limit the application to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, in some embodiments, for example, the candidate point of interest determination module 410, the relevant get-on point determination module 420, the recommended get-on point determination module 430, and the sending module 440 disclosed in fig. 4 may be different modules in one system, or may be one module to implement the functions of two or more modules described above. For example, the candidate point of interest determining module 410, the related get-on point determining module 420, and the recommended get-on point determining module 430 may be three modules, or may be one module having functions of determining candidate points of interest, related get-on points, and recommended get-on points at the same time. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the application.

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

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

In some embodiments, the search keywords may be various types of words, letters, numbers, characters, etc., or combinations thereof, entered by the user through the user terminal 130. After the user inputs the 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 and process the search keyword. In some embodiments, the manner in which the user enters the search keywords may include, but is not limited to, any combination of one or more of typewriting, handwriting, selection, voice, scan-in, and the like. In particular, typing may include English input, chinese input, and the like, depending on the language. The selection input may include selecting keywords or the like from a selection list. The scan input may include scan bar code input, scan two-dimensional code input, scan text input, scan picture input, and the like. For example, the search keyword may be chinese text that is directly handwritten by the user. For another example, the search keyword may be a text or letter identified from a user's scanned picture input. For another example, the search keyword may be a text or letter recognized from a voice input by the user.

In some embodiments, the search keywords may be used to search for places of interest to the user. For example, when a user uses the taxi taking software to take a taxi, keywords related to a start place, a get-on point, and/or a destination may be obtained by inputting search keywords. For example, a user may call other users by entering search keywords related to the boarding and disembarking of other users on a call platform. In some embodiments, the user-entered search keywords may include keywords related to place names, business circles, location attributes (e.g., hotels, malls, movie theatres, etc.), addresses, and the like. In some embodiments, determining at least one candidate point of interest from the search keywords may also be applied to the fields of map, 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 keywords may include: and determining a relevant interest point list according to the search keywords, predicting the selected probability of each relevant interest point in the list, and determining at least one candidate interest point according to the selected probability of each relevant interest point in the list. For more details on determining candidate points of interest from search keywords, see 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. Specifically, this step 520 may be performed by the relevant get-on point determination model 420.

In some embodiments, for example, when the candidate point of interest is a region (e.g., beijing university), it will be difficult to know the exact location where the user wishes to begin the journey by the candidate point of interest. Therefore, it is necessary to determine a boarding point (such as a south door of Beijing university) related to the candidate interest point based on the candidate interest point, so as to further clarify the boarding position of the user. In some embodiments, determining at least one relevant entry point for the at least one candidate point of interest may include: acquiring position information of at least one candidate interest point; and determining at least one relevant get-on point for the candidate interest point at least according to the position information of the candidate interest point. In some embodiments, the relevant get-on 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 get-on 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 on determining relevant pick-up points for candidate points of interest, see fig. 7-8 and their associated description.

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

In some embodiments, the recommended get-on point determination module 430 may obtain user location information, historical user information, and/or a degree of awareness of related get-on points, and determine at least one recommended get-on point from at least one related get-on point based on the obtained user location information, the historical user information, and/or the degree of awareness of related get-on points. In some embodiments, the recommended getting-on point determining module 430 may determine a text similarity of the search keyword and at least one of the related getting-on points, and screen at least one recommended getting-on point from the at least one related getting-on point according to the text similarity. For more details on determining a recommended get-on point from the relevant get-on points, see fig. 9-10 and their associated description.

In some embodiments, after the recommended get-on point module 430 determines the recommended get-on point, the sending module 440 may send the determined recommended get-on point to the user terminal 130 and display it. In some embodiments, a prompt marker may also be added to the recommended get-on point to guide the user's attention when displayed on the interface of the user terminal 130. For more details on the hint marks see FIG. 11 and the description thereof.

It should be noted that the above description of the process 500 is for purposes of illustration and description only and is not intended to limit the scope of the present application. Various modifications and changes to flow 500 may be made by those skilled in the art under the guidance of the present application. However, such modifications and variations are still within the scope of the present application. For example, in some embodiments, the relevant get-on points may be obtained directly from the search keywords. For another example, the recommended pick-up point may be determined directly from the candidate points of interest. For another example, after determining the recommended get-on point in step 530, a step of path planning may be added for planning a recommended path from the current location of the user to the recommended get-on point.

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

step 610, determining a related interest point list according to the search keywords. Specifically, 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. The related point of interest (Point of Interest, POI) may be a location related to the search keyword. Specifically, the related interest points may include information such as names, categories (e.g., point areas or area areas), longitude and latitude, and the like. In some embodiments, the related interest point determining unit 412 may retrieve the interest point having the same or similar name to the search keyword as the related interest point based on the keyword matching technique. The point of interest information may be stored in database 140. Specifically, the related-interest-point determining unit 412 may calculate the text similarity between the names of the interest points in the database 140 and the search keyword, thereby retrieving the interest point with higher text similarity as the related interest point, and forming the related interest point list. For example, the top n (e.g., 10, 15, 20, 30, etc.) points of interest of the text similarity may be selected as the relevant points of interest. For another example, points of interest having text similarity greater than a certain threshold (e.g., 60%, 80%, etc.) may be selected as the relevant points of interest. Any one of the available calculation methods in the prior art can be used for calculating the text similarity. For example, text similarity calculation may include calculating one or more of a Jaccard (Jaccard) similarity coefficient, cosine similarity, manhattan distance, euclidean distance, ming distance, edit distance, and the like, or any combination thereof. In some embodiments, other relevant factors, such as any combination of one or more of user location, user history search records, etc., may also be considered in determining the relevant point of interest list from the search keywords. For example, the related-interest-point determining unit 412 may consider only interest points within a certain range from the user (or the same city as the user, etc.) as related interest points.

In some alternative embodiments, the method of determining the relevant point of interest list from the search keywords may also be any suitable method in the prior art. For example, the list of relevant points of interest may be determined based on a prefix, core word or phrase, etc. of the search keyword. For another example, the list of relevant points of interest may be determined based on the speech, semantics, etc. of the search keyword. In some embodiments, before determining the relevant interest point list according to the search keyword, analysis, rewriting, error correction and other operations may be performed on the search keyword.

Step 620, predicting the selected probability of each relevant point of interest in the list. Specifically, step 620 may be performed by the selected probability prediction unit 414.

In some embodiments, the selection probability (or "click probability") may reflect a tendency of the user to select each relevant point of interest (or the on-board point related to the relevant point of interest). In some embodiments, the selected probability may be expressed as a probability that a point of interest (or a pick-up point associated with the point of interest) has historically been selected by a user (e.g., the user, or all users, or users within a specified area, etc.). In some embodiments, each of the related points of interest in the related point of interest list determined in step 610 may have a respective ranking score that may reflect the selected probability of each of the related points of interest, and thus the selected probability of each of the related points of interest may be determined based on the ranking scores. In some embodiments, the selected probabilities may be determined from a user's historical orders over a period of time. For example, the ratio of the total number of times a user selects a certain related interest point to the total amount of orders in the period of time may be determined as the probability of selection of the related interest point. In some embodiments, a click probability calculation model may be trained using historical orders over a period of time as samples, from which the click probability for each relevant point of interest is calculated. For example, the following features in the historical order may be extracted: the method comprises the steps of training an initial model by using extracted features to obtain a click probability calculation model, wherein the initial model comprises search keywords input by a user, interest point options provided for the user and interest points actually selected by the user. In some alternative embodiments, the probability of selection of each relevant point of interest in the list may also be predicted by other existing means, which is not limiting to the application.

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

In some embodiments, the server 110 may determine at least one candidate point of interest by way of 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 point of interest determination unit 416 may only select, as candidate points of interest, the relevant points of interest having a probability of being greater than or equal to 80% in the relevant point of interest list. In some embodiments, candidate point of interest determination unit 416 may determine the top N top-ranked (e.g., the highest probability of being selected) related points of interest in the related point of interest list as candidate points of interest, 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 determining relevant pick-up points according to some embodiments of the application. As shown in fig. 7, an exemplary flow 700 of 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 acquiring 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 point of interest may further include one or more of altitude, roads in the road network in which the candidate point of interest is located, areas in the road network in which the candidate point of interest is located, and the like. In some embodiments, the location information of the candidate points of interest may be obtained in a variety of ways, for example, the location information of the candidate points of interest may be queried or retrieved from a map database (e.g., database 140). For another example, the location information of the candidate point 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 get-on point for the candidate interest point at least according to the position information of the candidate interest point. Specifically, step 720 may be performed by the relevant get-on point determination unit 426.

In some embodiments, the related get-on point determination unit 426 may select at least one related get-on point for the candidate point of interest from a plurality of get-on points (e.g., a set of get-on points) according to the location information of the candidate point of interest. The set of boarding points may be a set of points on a transportation service platform (e.g., a taxi platform) where all passengers may board or where a driver may wait for passengers to board. In some embodiments, the set of get-on points may consist of all the get-on points in the historical order data on the taxi-taking platform. In some embodiments, the set of departure points may include any other location suitable as departure point, such as a first gate of a cell, an east gate of a hospital, a temporary stop point on a road network, and so forth. In some embodiments, at least one entry point within a certain radius range (e.g., 500m, 300m, 200m, 100m, etc.) may be determined to be a relevant entry point, centered on the location of the candidate point of interest (e.g., latitude and longitude coordinates used to determine the candidate point of interest). In some embodiments, all boarding points within a certain radius range may be considered as related 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 points. For example, the related getting-on points (e.g. screening) can be determined according to the similarity between the names of the getting-on points and the candidate interest point names. For example, a pick-up point whose name has a similarity to the candidate point of interest name greater than a certain set threshold (e.g., 50%, 60%, 70%, etc.) may be selected as the relevant pick-up point. For another example, a fixed number (e.g., 3, 5, 8, etc.) of boarding points may be selected as the relevant boarding points based on how far the boarding points are from the candidate points of interest.

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

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

In some embodiments, the face region may represent a regional geographic entity in the map data, e.g., beijing university/Baolixi Yue spring, etc. The dot area may represent a geographical entity that is dot-like in the map data, e.g., the university of Beijing, the university of south door, the university of Baolixi, the spring Siemens, etc. In some embodiments, the candidate points of interest may be retrieved in a map database (e.g., database 140), the tags of the candidate points of interest (or the tags of the candidate points of interest information themselves) may be obtained, and the candidate points of interest may be determined to be point areas or area areas based on the tag types. For example, the tag includes point data and face data, the candidate points of interest for which the tag is the point data are determined as the point areas, and the candidate points of interest for which the tag is the face data are determined as the face areas. In some embodiments, the point area or the area of the surface 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 greater than a certain set threshold (e.g., 100, 200, 500, 1000 square meters), the candidate interest point may be considered as a face region; otherwise, the candidate point of interest may be identified as a point region. In some embodiments, the determination of the dot area or the area of the surface may also be made by other factors. For example, the candidate point of interest may be determined to be a point region or a plane region based on factors such as the perimeter of the candidate point of interest region, the straight line distance of the farthest two points in the region, the walking distance (or walking time) of the farthest two points in the region, and the like.

Step 820, determining at least one relevant get-on point based at least on the determination that the at least one candidate point of interest is a point area or a face area. Specifically, step 820 may be performed by the relevant get-on point determination unit 426.

In some embodiments, when the candidate point of interest is a point region, it may be determined that the candidate point of interest is a relevant entry point. In some embodiments, when the candidate point of interest is a face region, at least one get-on point that is within the range of the candidate point of interest may be determined to be a relevant get-on point. In some embodiments, when the candidate point of interest is a surface area, at least one get-on point having a distance within a certain range (e.g., a linear distance from any point in the surface area of no more than 20m, 30m, 50m, 100m, etc.) may also be determined as the relevant get-on point. For more details on determining relevant pick-up points for candidate points of interest, see fig. 7 and its associated description.

FIG. 9 is an exemplary flow chart for determining recommended pick-up points from among related pick-up points according to some embodiments of the application. Specifically, the process 900 of determining recommended pick-up points from the associated 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 drive-up point determination module 430 may obtain user location information, historical drive-up information for the user, and/or awareness of relevant drive-up points.

In some embodiments, the user position acquisition unit 431 may acquire the user position information. In particular, 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 at which 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 a positioning technique and transmit 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 the positioning technology, that is, the user location information. In particular, the positioning technology may include, but is not limited to, any combination of one or more of global positioning system (GPS, global Positioning System), satellite positioning technology, beidou positioning technology, near field positioning (e.g., wifi positioning, bluetooth positioning), etc.

In some embodiments, the history information acquisition unit 432 may acquire user history car information. Specifically, the user historical vehicle information may include historical departure points, historical destination points, historical departure times, user credits, user historical evaluations, and the like, or any combination thereof. In some embodiments, the user historical use vehicle information may include at least historical point of use information. In some embodiments, the history information acquisition unit 432 may acquire the user history car information from the database 140. For example, processing device 112 may obtain and/or process user historical order information including user historical use vehicle information and store in database 140. The information acquisition unit 432 may acquire a user ID that may uniquely determine user identity information and further call the historical order information in the database 140 through the user ID to acquire user historical vehicle information corresponding to the user ID. In some embodiments, the user historical occupancy information may be used to obtain user preference habits (e.g., the occupancy points and features thereof for which the user is accustomed) and further to determine recommended occupancy points.

In some embodiments, the get-on point information acquiring unit 433 may acquire the awareness of the relevant get-on point. Specifically, the awareness of the relevant pick-up points may include search heat, identification, etc., or a combination thereof. For example. The search popularity may include the number of times (or frequency, probability, etc.) the user searches for and/or selects the relevant pick-up point, and the identification may include whether the relevant pick-up point is a business turn, has a readily identifiable logo (e.g., building, sculpture, housing, etc.). The get-on point information acquiring unit 433 may acquire the awareness information of the relevant get-on point from the database 140. In some embodiments, the awareness of the relevant pick-up points may be expressed as a score value, with the higher the score value, the higher the awareness and vice versa. The score value may be obtained based on historical search conditions of related boarding points, user evaluation conditions, worker scores, and the like. In some embodiments, the known degree of the relevant pick-up point may be used to determine whether the relevant pick-up point is easily perceived and further used to determine the recommended pick-up point.

In some embodiments, the recommended get-on point determination module 430 may also obtain other information related to the user and/or the relevant get-on point. For example, the dockable attribute of the relevant pick-up point may be obtained. In particular, the dockable attribute of the relevant get-on point may include the road condition of the road on which the relevant get-on point is located (e.g., one-way road, two-way road, congestion, whether there is a roadblock, whether it is a highway exit, etc.), the number of drivers near the relevant get-on point, etc. In some embodiments, the dockable properties of the relevant get-on points may be used to calculate information such as user walking costs, driver pick-up costs, and the like, and further used to determine recommended get-on points.

Step 920, at least one recommended get-on point is selected from at least one relevant get-on point according to the user location information, the historical use information of the user and/or the awareness of the relevant get-on points. Specifically, step 920 may be performed by the recommended get-on point determination unit 435.

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

In some embodiments, the recommended getting-on point determining unit 435 may determine at least one recommended getting-on point from at least one relevant getting-on point according to the historical getting-on information of the user. For example, the recommended getting-on point determining unit 435 may determine at least one (e.g., 1, 2, 3, etc.) getting-on point selected the most from the history of the user among the relevant getting-on points as the recommended getting-on point based on the history of the user. In some embodiments, the user historical use vehicle information may be the user's own historical use vehicle information. In some embodiments, the user historical use vehicle information may also include historical use vehicle information for other users associated with the user. For example, the associated other user may be a home address, a corporate address, etc. of the other user that is the same as or similar to the user.

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

In some embodiments, the recommended getting-on point determining unit 435 may further determine at least one recommended getting-on point from at least one relevant getting-on point by comprehensively considering two or all of the user location information, the historical getting-on information of the user, and the awareness of the relevant getting-on points. For example, different weights may be set for the walking cost of the user, the historical selection times of the user, and the awareness of the relevant get-on points, the comprehensive score of each relevant get-on point may be calculated, and at least one (e.g., 1, 2, 3, 4, 5, etc.) relevant get-on point with the highest comprehensive score may be selected as the recommended get-on point.

FIG. 10 is an exemplary flow chart for determining recommended pick-up points from among related pick-up points according to some embodiments of the 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 of determining a recommended boarding may include:

And step 1010, determining the text similarity between the search keyword and at least one relevant get-on point. 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 keyword. 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 spelling errors in the search keywords and correct them to obtain the preprocessed search keywords. For another example, the search keyword may be subjected to word segmentation processing (e.g., "beijing road KFC" is processed as "beijing road/KFC"). Further, the text similarity determination unit 434 may determine the text similarity of the search keyword or the preprocessed search keyword to each of the related getting-on points. In some embodiments, the similarity of the search keywords (or the preprocessed search keywords) to the relevant get-on points may be calculated based on a text similarity algorithm. Specifically, the text similarity algorithm may include a cosine similarity method, a simple common word 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.

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

FIG. 11 is a schematic diagram of a display interface for prompting a recommended get-on point according to some embodiments of the application. As shown in fig. 11, the display interface may include a search input area, a get on point display area, and the like. In this embodiment, after the user inputs the departure point name 1102 in the search input area of the display interface, a plurality of related boarding points related to the departure point name are displayed on the interface. Specifically, the information of the roll-up names 1108 (including 1108-1, 1108-2), the roll-up points 1110 (including 1110-1, 1110-2) and the like of the relevant roll-up points are displayed on the interface. Corresponding to the recommended entry points in the relevant entry points, a prompt mark 1106 for guiding the user to select or pay attention is displayed on the interface in addition to the names 1104 of the recommended entry points. The hint marks that guide the user's selection or attention may be text hints, font bolding, font highlighting, font underlining, and the like. For example, when a user inputs "Beijing university" on the interface, the get-on-point display area on the interface displays a plurality of related get-on points (e.g., beijing university Dongmen [ subway station ], chinese civil bank (branch line of the manway) opposite, beijing university oral hospitals, etc.) in a list manner in real time, and recommended get-on points (e.g., beijing university-Dong 2 door, beijing university-Dong A mouth northwest exit) among the related get-on points may be displayed in the front of the list. In addition, these recommended get-on points may be labeled with a "recommended" word, thereby making it easier for the user to select these recommended get-on points. The distance from the get-on point to the current position of the user can also be displayed beside each get-on point so as to be convenient for the user to refer to. In some alternative embodiments, hint marks 1106 can be further refined. Specifically, the recommended reason may be displayed beside the recommended get-on point. For example, the selection probability, the number of people selected, the search heat, the walking distance, the walking time, etc. can be displayed beside the recommended get-on point to prompt and guide the user to select.

The possible beneficial effects of the embodiment of the application include but are not limited to: (1) By providing recommended boarding points for passengers, the probability of selecting unreasonable boarding points is reduced; (2) improving the passenger travel experience; (3) The driving receiving efficiency of the driver is improved, for example, the long-term benefit of the driver is higher; (4) improving the overall benefit of the network taxi platform. It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.

Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.

Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.

The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. 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 through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.

The computer program code necessary for operation of 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, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or 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 form of network, 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 the use of services such as software as a service (SaaS) in a cloud computing environment.

Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative 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 included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are required by the subject application. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.

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 application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the 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 keywords;

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

when the candidate interest points are surface areas, at least one relevant get-on point is determined for the at least one candidate interest point at least according to the position information of the candidate interest points;

determining at least one relevant get-on point as a recommended get-on point; wherein the determining at least one relevant get-on point as a recommended get-on point comprises: and determining the at least one relevant get-on point as the recommended get-on point based on the berthable attribute of the at least one relevant get-on point.

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

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

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 recommended get-on points of claim 1, wherein the determining at least one relevant get-on point for the candidate point of interest based at least on location information of the candidate point of interest comprises:

Determining at least one historical driving point within a certain range from the candidate interest point according to the position information of the candidate interest point;

the at least one historical entry point is determined as the at least one relevant entry point.

4. The method of determining recommended get-on points of claim 1, wherein the determining at least one relevant get-on point for the at least one candidate point of interest comprises:

judging that the at least one candidate interest point is a point area or a surface area;

and determining at least one relevant get-on point at least according to the judgment that the at least one candidate interest point is a point area or a surface area.

5. The method of claim 4, wherein determining at least one relevant entry point based on the determination that the at least one candidate point of interest is a point area or a face area comprises:

when the candidate interest points are the point areas, determining that the candidate interest points are relevant get-on points;

when the candidate interest point is a face region, at least one relevant get-on point located within the candidate interest point is determined.

6. The method of determining a recommended entry point of claim 1, wherein said determining at least one of said associated entry points as a recommended entry point comprises:

Acquiring user position information;

and screening at least one recommended get-on point from at least one relevant get-on point according to the user position information.

7. The method of determining a recommended entry point of claim 1, wherein said determining at least one of said associated entry points as a recommended entry point comprises:

acquiring historical vehicle information of a user;

and screening at least one recommended get-on point from at least one relevant get-on point according to the historical get-on information of the user.

8. The method of determining a recommended entry point of claim 1, wherein said determining at least one of said associated entry points as a recommended entry point comprises:

acquiring the awareness degree of the related boarding points;

and screening at least one recommended get-on point from at least one relevant get-on point according to the awareness.

9. The method of determining a recommended entry point of claim 1, wherein said determining at least one of said associated entry points as a recommended entry point further comprises:

and screening at least one recommended get-on point from at least one relevant get-on point based on the search keyword.

10. The method of determining recommended entry points of claim 9, wherein said selecting at least one recommended entry point from at least one of said associated entry points based on said search keywords comprises:

Determining the text similarity between the search keyword and at least one relevant get-on point;

and screening at least one recommended get-on point from at least one relevant get-on point according to the text similarity.

11. The method of determining a recommended get-on point of claim 1, further comprising:

and sending the at least one recommended get-on point to a terminal, and enabling the at least one recommended get-on point to have a prompt mark for guiding the attention of a user when the terminal is displayed.

12. The system for determining the recommended get-on point is characterized by comprising a candidate interest point determining module, a related get-on point determining module and a recommended get-on point determining module; wherein,

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

the related getting-on point determining module is used for obtaining the position information of the at least one candidate interest point, and determining at least one related getting-on point for the at least one candidate interest point at least according to the position information of the candidate interest point when the candidate interest point is a surface area;

the recommended get-on point determining module is used for determining at least one relevant get-on point as a recommended get-on point; the recommended get-on point determining module is further configured to: and determining the at least one relevant get-on point as the recommended get-on point based on the berthable attribute of the at least one relevant get-on point.

13. The system for determining recommended get-on points of claim 12, wherein the candidate point of interest determination module further comprises: the device 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 keywords;

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

14. The system for determining recommended entry points of claim 12, wherein the relevant entry point determination unit is further configured to:

15. The system for determining recommended drive points according to claim 12, wherein the related drive point determination module further comprises a region judgment unit and a related drive point determination unit; wherein,

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

the related get-on point determining unit is configured to determine at least one related get-on point at least according to a determination that the at least one candidate interest point is a point area or a surface area.

16. The system for determining recommended entry points of claim 15, wherein the relevant entry point determination unit is further configured to:

17. The system for determining a recommended get-on point according to claim 12, wherein the recommended get-on point determination module further comprises a user position acquisition unit and a recommended get-on point determination unit; wherein,

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

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

18. The system for determining a recommended get-on point according to claim 12, wherein the recommended get-on point determination module further includes a history information acquisition unit and a recommended get-on point determination unit; wherein,

The history information acquisition unit is used for acquiring history vehicle information of a user;

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

19. The system for determining a recommended get-on point according to claim 12, wherein the recommended get-on point determination module further comprises a get-on point information acquisition unit and a recommended get-on point determination unit; wherein,

the get-on point information acquisition unit is used for acquiring the awareness degree of the related get-on points;

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

20. The system for determining a recommended get-on point of claim 12, wherein the recommended get-on point determination module is further configured to:

21. The system for determining a recommended get-on point of claim 20, wherein the recommended get-on point determination module further comprises a text similarity determination unit and a recommended get-on point determination unit; wherein,

The text similarity determining unit is used for determining the text similarity of the search keyword and at least one relevant get-on point;

the recommended get-on point determining unit is used for screening at least one recommended get-on point from at least one related get-on point according to the text similarity.

22. The system for determining a recommended get-on point of claim 12, further comprising a transmission module for:

23. An apparatus for determining recommended pick-up points comprising at least one storage medium and at least one processor, characterized in that,

the at least one storage medium is for storing computer instructions;

the at least one processor is configured to execute the computer instructions to implement the method for determining a recommended get-on point according to any one of claims 1-11.

24. A computer readable storage medium storing computer instructions which, when executed by a computer, implement the method of determining a recommended pick-up point of any of claims 1 to 11.